Visium data - 'Baseline' samples: Main processing and analysis using Seurat/STUtility
L. Franzén lovisa.franzen@scilifelab.se
April 22, 2021
Description
Data processing and analysis of Visium data of subcutaneous white adipose tissue (scWAT) from 10 subjects at fasting state.
The standard Visium protocol (Rev A) as published by 10x Genomics was applied to generate the Visium sample libraries. Specific modifications of the protocol to adapt it for adipose tissue included using a section thickness of 16 µm and permeabilizing the tissue using a 15 min incubation with the permeabilization enzyme.
Sequencing of the Visium libraries was performed on the Illumina NovaSeq6000 platform, using a P4 flow cell, resulting in approximately 90M read-pairs per sample. The sequencing was performed by the National Genomics Infrastructure in Genomics Production Stockholm.
Processing of the FastQ sequencing files was performed using the Spaceranger (version 1.0.0) pipeline, mapping to the GRCh38-3.0.0 genome. Manual selection of spots to include in the analysis was performed using the Loupe Browser.
Analysis of the Spaceranger output data was performed using STUtility, which in turn makes use of the Seurat version 3, (3.1.5) R package.
The main data processing/analysis steps include the following:
- Initialize: Set up Seurat object and perform initial filtering of spots and genes
- QC: Further filter spots and genes based on specified criteria
- SCTransform: Peform normalization and scaling of the data using the 7000 most variable genes across the data points
- Dimensionality reduction: Independent Component Analysis (ICA) followed by Harmony (v 1.0) to remove subject an batch differences. The Harmony vectors, of which some uninformative vectors had been excluded, were used as input for UMAP and cluster analysis
- Clustering: Performed using the
FindNeighbors()andFindClusters(algorithm = 1, resolution = k)(Louvain) functions. Clustering resolution k = 1.0 was selected after manual inspection of various resolution alternatives
- Marker genes: Cluster markers were identified using the
FindMarkers()andFindAllMarkers(only.pos = TRUE, min.pct = 0.1, logfc.threshold = 0.15)functions
Initialize
Load necessary tables
Sample meta data
metadata <- read.table(file = file.path(DIR_DATA, "visium_sample_metadata.tsv"), sep = "\t", header = T, stringsAsFactors = F)
metadata_selected <- metadata[is.na(metadata$insulin_stim) | metadata$insulin_stim == 0, ] # all baseline samples (no ins stim)
metadata_selected$seu_n <- seq( 1, dim(metadata_selected)[1] )
datatable(metadata_selected, rownames = F, caption = paste("Sample metadata"))Gene annotations
gene_table <- read.table(file = file.path(DIR_DATA, "..", "gene_annotation", "gene_table.tsv"), sep = "\t", header = T, stringsAsFactors = F )InfoTable
sample_select_ids <- metadata_selected[, "novaseq_id"]
data_proc <- "trimmed"
sample_dirs_all <- c()
sample_names_all <- c()
for (s in sample_select_ids){
message("Fetching data path for sample ", s)
sample_dirs <- grep(pattern = s, x = list.dirs(path = file.path(DIR_DATA), recursive = F, full.names = T), value = T)
sample_dirs <- grep(pattern = data_proc, x = sample_dirs, value = T)
sample_dirs_all <- c(sample_dirs_all, sample_dirs)
sample_names <- grep(pattern = s, x = list.dirs(path = file.path(DIR_DATA), recursive = F, full.names = F), value = T)
sample_names <- grep(pattern = data_proc, x = sample_names, value = T)
sample_names_all <- c(sample_names_all, sample_names)
}
data_dirs <- paste0(sample_dirs_all, "/filtered_feature_bc_matrix.h5")
spot_dirs <- paste0(sample_dirs_all, "/spatial/tissue_positions_list.csv")
img_dirs <- paste0(sample_dirs_all, "/spatial/tissue_hires_image.png")
img_scale_dirs <- paste0(sample_dirs_all, "/spatial/scalefactors_json.json")
infoTable <- data.frame(samples = data_dirs,
spotfiles = spot_dirs,
imgs = img_dirs,
json = img_scale_dirs,
metadata_selected,
stringsAsFactors = F)Create Seurat object
Since we only have few cells per spot, we are a bit more gentle with the filtering compared to what is common for other tissue types.
se <- InputFromTable(infotable = infoTable,
min.gene.count = 100,
min.gene.spots = 5,
min.spot.count = 200,
platform="Visium")## Using spotfiles to remove spots outside of tissue
## Loading /Users/lovisa.franzen/Documents/ST_Adipose/scwat-st/scripts/../data/visium/S42_trimmed/filtered_feature_bc_matrix.h5 count matrix from a 'Visium' experiment
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##
## ------------- Filtering (not including images based filtering) --------------
## Spots removed: 605
## Genes removed: 21468
## Saving capture area ranges to Staffli object
## After filtering the dimensions of the experiment is: [12070 genes, 28360 spots]QC
se <- PercentageFeatureSet(se, pattern = "^MT-", col.name = "percent.mt")
se <- PercentageFeatureSet(se, pattern = "^MTRNR", col.name = "percent.MTRNR")
se <- PercentageFeatureSet(se, pattern = "^MALAT1", col.name = "percent.MALAT1")
se <- PercentageFeatureSet(se, pattern = "^RPS|^RPL", col.name = "percent.RP")
se <- PercentageFeatureSet(se, features = c("HBB", "HBA2", "HBA1"), col.name = "percent.HB.genes")#' Subset/filter data before normalizing and scaling data. Remove high MT-content spots, and high HB-content spots
dim(se)## [1] 12070 28360paste("n spots with too high MT-content:", dim(subset(se[[]], percent.mt >= 40))[1] )## [1] "n spots with too high MT-content: 244"paste("n spots with too high HB-content:", dim(subset(se[[]], percent.HB.genes >= 10))[1] )## [1] "n spots with too high HB-content: 179"spots_mt <- rownames(subset(se[[]], percent.mt < 40))
spots_hb <- rownames(subset(se[[]], percent.HB.genes < 10))
spots <- intersect(spots_mt, spots_hb)
#' FILTER 1
se <- SubsetSTData(se, spots = spots)
#' Filter genes: Updated list
genes_remove <- grep("^HBB|^HBA|^MTRNR|^MT-|^MALAT1|^NEAT1|^RPS|^RPL", x = rownames(se), value = TRUE)
#' Filter gene types
gene_type_remove <- "antisense" # c(grep(pattern = "pseudogene", x = unique(gene_table$gene_type), value = T), "antisense")
genes_remove_2 <- c(genes_remove, gene_table[gene_table$gene_type %in% gene_type_remove, "gene_name"])
paste("Removing", length(genes_remove), "genes due to specific selection")## [1] "Removing 117 genes due to specific selection"paste("Removing", length(intersect(rownames(se), genes_remove_2)),
"genes in total when excluding biotype 'antisense'")## [1] "Removing 380 genes in total when excluding biotype 'antisense'"#' Define genes to keep
genes_keep <- setdiff(rownames(se), genes_remove_2)
#' FILTER 2
se <- se[genes_keep, ]
dim(se)## [1] 11690 27937SCTransform
n_var_feat <- 7000
se <- SCTransform(se, variable.features.n = n_var_feat, verbose = F)Dimensionality reduction
ICA
Warning: This step takes a long time to run due to the high number of spots and genes in the data
se <- RunICA(object = se, verbose = T)## IC_ 1
## Positive: SAA1, AHNAK, LEP, HSP90AA1, SPTBN1, ITGB1, CRYAB, AKAP12, DDR2, GOLGA4
## PALMD, CCDC80, KIF5B, UTRN, CES1, PLIN4, SORBS1, CALM1, TNS1, ITIH5
## CAVIN1, GOLGB1, ITSN1, NCL, KTN1, CHRDL1, GPAM, HSP90B1, DST, MAP1B
## Negative: ADIRF, APOC1, ADH1B, FAU, APOE, GLUL, FASN, EIF1, FABP5, EEF1A1
## CFD, CIDEA, FABP4, COX7C, TTC36, UBA52, EEF2, CYB5A, RACK1, ATP5MC2
## ZFAND5, PRDX6, UQCRB, NACA, MZT2B, ATP5ME, SLC25A6, SNHG25, NDUFA4, TOMM7
## IC_ 2
## Positive: AHNAK, HSP90AA1, SCD, SPTBN1, GOLGA4, DDR2, KTN1, ITGB1, CAVIN1, CCDC80
## DCN, NCL, TTC3, AKAP12, EIF3A, KIF5B, GOLGB1, MAP1B, ATRX, LEP
## HSP90B1, UTRN, EIF5B, CAST, IGFBP5, DST, CALD1, ANKRD12, MYCBP2, CSDE1
## Negative: G0S2, FABP4, FTL, MT1X, ADIRF, GPX4, DGAT2, CRYAB, PLIN4, RBP4
## MGST3, POLR2L, ATP5MD, COX7C, AGPAT2, LGALS1, UQCRH, COX6B1, RARRES2, GPD1
## OST4, NDUFA4, FAU, UQCR10, S100A11, UQCR11, UBL5, TSTD1, COX7B, FTH1
## IC_ 3
## Positive: EGFL6, SAA1, CES1, TNMD, ASAH1, TMSB4X, MEGF9, CACNA2D1, COL3A1, AKR1C1
## CLU, AKR1C2, AKR1C3, ANXA5, STMN2, LEP, PALLD, SEMA3C, ITIH5, SNX10
## NPR3, VGLL3, SAA2, NPY1R, NQO1, UGP2, TUBB2A, LRMDA, SFRP2, PLIN2
## Negative: FASN, AKAP12, PABPC1, TNS1, GLUL, GPX3, ADH1B, PFKFB3, ADIRF, EEF1A1
## FADS1, CD81, HLA-B, HLA-A, PTMA, CXCL14, NNAT, UBA52, SCD, TF
## ACACB, MT1X, AQP7, SORBS1, CFD, FAU, NAP1L1, CD74, A2M, B2M
## IC_ 4
## Positive: ACKR1, CD74, AQP1, VWF, RAMP3, PLVAP, HLA-B, HLA-E, HLA-DRA, IGFBP7
## PECAM1, HLA-DRB1, CTGF, IFITM3, HLA-DPA1, B2M, TAGLN, PLAT, HLA-A, IGFBP5
## ENG, EDN1, HLA-DPB1, TSPAN7, SPARCL1, IGFBP4, ACTA2, MLPH, KCTD12, IFITM2
## Negative: SAA1, FABP4, LEP, G0S2, PLIN1, CRYAB, GPAM, SCD, ADIPOQ, ACSL1
## PLIN4, PPP1R1A, MGST1, PRKAR2B, RBP4, GOLGA4, GPD1, SORBS1, GPX4, ITGB1
## SPTBN1, HSP90AA1, PLA2G16, AHNAK, AKAP12, AKR1C2, CAV2, TRDN, ADH1B, ALDH2
## IC_ 5
## Positive: SAA1, AHNAK, SCD, ITGB1, HSP90AA1, LEP, SPTBN1, GPAM, DDR2, ACSL1
## PALMD, NCL, AKAP12, GOLGA4, CALD1, SORBS1, KTN1, CES1, KIF5B, PLIN4
## PTPN11, UTRN, TTC3, DST, UGP2, CAVIN1, MAP1B, MGST1, GPD1, MAP4
## Negative: RNASE1, C1QA, C1QB, LGMN, FOLR2, C1QC, LYVE1, SELENOP, F13A1, TYROBP
## CD74, STAB1, CD14, CSF1R, MARCO, CST3, CTSC, MRC1, FCGR2B, MS4A4A
## PLTP, FCGRT, MPEG1, FCER1G, MS4A6A, LILRB5, SLC40A1, TGFBI, CD68, FTLHarmony
Integration of data from the different donors, and batch effect removal, using Harmony. Harmony is a method for integration of single cell RNA-seq data but we've seen promising use for it on ST data. It can easily be run together with Seurat using the RunHarmony() function.
http://htmlpreview.github.io/?https://github.com/immunogenomics/harmony/blob/master/docs/SeuratV3.html
*Korsunsky, I., Millard, N., Fan, J. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods 16, 1289–1296 (2019). https://doi-org.focus.lib.kth.se/10.1038/s41592-019-0619-0*
var_int <- c("date_exp", "subject_id")
se <- RunHarmony(object = se, group.by.vars = var_int, assay.use="SCT", reduction = "ica", plot_convergence = TRUE)
Dimension selction
Based on manual visual inspection of the vector loading for each harmony component, the most prominent and biologically relevant components were selected for further processing.
red_use <- "harmony"
dims_use <- 1:35UMAP
nneigh <- 50
se <- RunUMAP(object = se,
reduction = red_use,
dims = dims_use,
n.neighbors = nneigh)
se <- RunUMAP(object = se,
reduction = "ica",
dims = dims_use,
n.neighbors = nneigh,
reduction.name = "umapICA",
seed.use = 42)
se <- AddMetaData(se, se@reductions$umap@cell.embeddings, col.name = c("UMAP_1", "UMAP_2"))paste("k (spots):", dim(se@reductions$umap@cell.embeddings)[1])## [1] "k (spots): 27937"Clustering
Finding neighbours by constructing a Shared Nearest Neighbor (SSN) Graph, and then cluster using a modularity optimizer.
Generate clusters
se <- FindNeighbors(object = se, verbose = T, reduction = red_use, dims = dims_use)
for (res in c(0, 0.2, seq(0.4, 1, 0.1), 1.2, 1.6, 2) ){
print(res)
se <- FindClusters(object = se, verbose = T, algorithm = 1, resolution = res)
}## [1] 0
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 27937
## Number of edges: 563101
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 1.0000
## Number of communities: 21
## Elapsed time: 1 seconds
## [1] 0.2
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 27937
## Number of edges: 563101
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9037
## Number of communities: 31
## Elapsed time: 1 seconds
## [1] 0.4
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 27937
## Number of edges: 563101
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8334
## Number of communities: 34
## Elapsed time: 2 seconds
## [1] 0.5
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 27937
## Number of edges: 563101
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8021
## Number of communities: 35
## Elapsed time: 2 seconds
## [1] 0.6
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 27937
## Number of edges: 563101
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7741
## Number of communities: 36
## Elapsed time: 3 seconds
## [1] 0.7
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 27937
## Number of edges: 563101
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7479
## Number of communities: 38
## Elapsed time: 2 seconds
## [1] 0.8
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 27937
## Number of edges: 563101
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7337
## Number of communities: 38
## Elapsed time: 3 seconds
## [1] 0.9
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 27937
## Number of edges: 563101
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7213
## Number of communities: 39
## Elapsed time: 3 seconds
## [1] 1
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 27937
## Number of edges: 563101
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7103
## Number of communities: 40
## Elapsed time: 4 seconds
## [1] 1.2
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 27937
## Number of edges: 563101
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.6921
## Number of communities: 42
## Elapsed time: 4 seconds
## [1] 1.6
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 27937
## Number of edges: 563101
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.6639
## Number of communities: 44
## Elapsed time: 4 seconds
## [1] 2
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 27937
## Number of edges: 563101
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.6432
## Number of communities: 47
## Elapsed time: 4 secondsres_use <- "SCT_snn_res.1"
se[["seurat_clusters"]] <- se[[res_use]]
se[["seurat_clusters"]] <- as.factor(as.numeric(as.character(se[[]]$seurat_clusters))+1) #as.factor(as.numeric(se[[]]$seurat_clusters))
se <- SetIdent(se, value = "seurat_clusters")
n <- length( unique(as.character(se@meta.data$seurat_clusters)) )
print(paste("Number of clusters:", n))## [1] "Number of clusters: 20"Cluster annotations
cluster_anno <- c(
"Unspecific 1",
"Unspecific 2",
"Adipocyte 1",
"Adipocyte 2",
"Endothelial 1",
"Adipocyte 3",
"Preadipocyte 1",
"Immune: Mac-Mono-DC 1",
"Preadipocyte 2",
"Immune: Mast cell",
"Preadipocyte 3",
"Preadipocyte 4",
"Immune: NK cell",
"Endothelial 2",
"Immune: Mac-Mono-DC 2",
"Immune: Mac-Mono-DC 3",
"Vascular 1",
"Immune: B cell",
"Vascular 2",
"Immune: Mac-Mono-DC 4")
cluster_group <- c(
"Unspecific", "Unspecific",
"Adipocyte", "Adipocyte",
"Vascular",
"Adipocyte",
"Fibroblast/Pread",
"Immune",
"Fibroblast/Pread",
"Immune",
"Fibroblast/Pread",
"Fibroblast/Pread",
"Immune",
"Vascular",
"Immune", "Immune",
"Vascular",
"Immune",
"Vascular",
"Immune"
)
c_anno <- data.frame(seurat_clusters = as.numeric(seq(1, 20)),
cluster_anno = cluster_anno,
cluster_group = cluster_group,
stringsAsFactors = F)
c_anno <- c_anno[order(as.character(c_anno$cluster_anno)),]
cluster_anno <- NULL
cluster_group <- NULL
c_anno$cluster_color <- "#FFFFFF"
c_anno$color_group <- "white"
color_func_blue <- colorRampPalette(colors = c("#C6DBEF", "#075a84"))
color_func_orange <- colorRampPalette(colors = c("#F3E55C", "#E8602D"))
color_func_pink <- colorRampPalette(colors = c("#bfabc3", "#62376e"))
color_func_green <- colorRampPalette(colors = c("#a6dbbb", "#359566"))
color_func_purp <- colorRampPalette(colors = c("#ecd9f1", "#967bce"))
for( group in unique(c_anno$cluster_group) ){
print(group)
gs <- c_anno[c_anno$cluster_group == group, "cluster_group"]
gs_l <- length(gs)
c_add <- 1
if(group == "Unspecific"){
col_pal <- brewer.pal(gs_l+c_add+5, "Greys")[(2+c_add):(gs_l+c_add+1)]
c_anno[c_anno$cluster_group == group, "color_group"] <- "grey"
}
if(group == "Adipocyte"){
# col_pal <- (brewer.pal(gs_l+c_add, "RdPu")[(1+c_add):(gs_l+c_add)])
col_pal <- color_func_pink(gs_l)
c_anno[c_anno$cluster_group == group, "color_group"] <- "purple"
}
if(group == "Fibroblast/Pread"){
# col_pal <- (brewer.pal(gs_l+c_add+1, "Oranges")[(1+c_add):(gs_l+c_add)])
col_pal <- color_func_orange(gs_l)
c_anno[c_anno$cluster_group == group, "color_group"] <- "orange"
}
if(group == "Immune"){
# col_pal <- (brewer.pal(gs_l+c_add+1, "Blues")[(1+c_add):(gs_l+c_add)])
col_pal <- color_func_blue(gs_l)
c_anno[c_anno$cluster_group == group, "color_group"] <- "blue"
}
if(group == "Vascular"){
# col_pal <- (brewer.pal(gs_l+c_add, "YlGn")[(1+c_add):(gs_l+c_add)])
col_pal <- color_func_green(gs_l)
c_anno[c_anno$cluster_group == group, "color_group"] <- "green"
}
print(col_pal)
c_anno[c_anno$cluster_group == group, "cluster_color"] <- as.character(col_pal)
}## [1] "Adipocyte"
## [1] "#BFABC3" "#907198" "#62376E"
## [1] "Vascular"
## [1] "#A6DBBB" "#80C39E" "#5AAC82" "#359566"
## [1] "Immune"
## [1] "#C6DBEF" "#A6C5DD" "#86B0CB" "#669AB9" "#4684A7" "#266F95" "#075A84"
## [1] "Fibroblast/Pread"
## [1] "#F3E55C" "#EFB84C" "#EB8C3C" "#E8602D"
## [1] "Unspecific"
## [1] "#D9D9D9" "#BDBDBD"c_anno_long <- se[["seurat_clusters"]]
c_anno_long$barcode <- rownames(c_anno_long)
c_anno_long2 <- merge(c_anno_long, c_anno, by = "seurat_clusters", sort = F)
rownames(c_anno_long2) <- c_anno_long2$barcode
c_anno_long2 <- c_anno_long2[rownames(c_anno_long), ]
se <- AddMetaData(se, c_anno_long2[, -c(1:2)])fname <- paste0(ANALYSIS_ID, ".clustering_annotations.csv")
write.csv(c_anno, file = file.path(DIR_RES, "tables", fname), row.names = F)Cluster stats
cluster_prop <- se[[]]
cluster_prop <- cluster_prop %>%
group_by(subject_alias, seurat_clusters, condition, bmi) %>%
dplyr::count()
cluster_prop <- as.data.frame(cluster_prop)
colnames(cluster_prop)[1] <- "subject"
subject_spot_count <- aggregate(cluster_prop$n, by=list(subject = cluster_prop$subject), FUN=sum)
colnames(subject_spot_count)[2] <- "total_spots"
cluster_prop <- merge(x = cluster_prop, y = subject_spot_count, by = c("subject"))
cluster_prop$cluster_pct_subject <- round((cluster_prop$n / cluster_prop$total_spots), digits = 3)*100
write.csv(cluster_prop, file = file.path(DIR_RES, "tables", paste0(ANALYSIS_ID, ".clustering_countspots.csv")), row.names = F)
datatable(cluster_prop, rownames = F, caption = paste("Cluster spot count and proportions"))Adipocyte re-clustering
Re-clustering of only the adipocyte clusters in order get more refined clusters and marker gene lists.
spots_select <- rownames(subset(se[[]], cluster_group %in% "Adipocyte"))
se_adi <- SubsetSTData(se, spots = spots_select)
dim(se_adi)## [1] 11690 6655unique(se_adi$cluster_anno)## [1] "Adipocyte 2" "Adipocyte 1" "Adipocyte 3"# ICA
se_adi <- RunICA(object = se_adi, verbose = T)## IC_ 1
## Positive: FTL, GPX4, G0S2, ADIRF, FTH1, PLIN4, MT1X, FAU, SAA1, LGALS1
## CRYAB, FHL1, OST4, ATP5MD, SERF2, SNHG25, UBA52, COX7C, POLR2L, S100A11
## TMSB10, NDUFA4, UQCR11, GPD1, TOMM7, PPP1R14A, AGPAT2, LIPE, COX6B1, S100A6
## Negative: AHNAK, SPTBN1, HSP90AA1, SCD, ITGB1, GPAM, KTN1, GOLGA4, GOLGB1, AKAP12
## DDR2, UTRN, DMD, ATRX, HSP90B1, EIF3A, NCL, ACSL1, UACA, DST
## KIF5B, SYNM, PDE3B, EIF5B, TTC3, CALD1, PRKAR2B, DYNC1H1, HMGB1, MAP4
## IC_ 2
## Positive: ADH1B, ADIRF, FAU, TTC36, APOE, APOC1, GLUL, FASN, FABP5, CFD
## CIDEA, EIF1, EEF1A1, FABP4, COX7C, EEF2, C6, ZFAND5, RACK1, PRDX6
## UBA52, ATP5ME, ATP5MC2, CYB5A, NACA, UQCRB, SNHG25, PFDN5, TOMM7, WNT3
## Negative: SAA1, LEP, AHNAK, SAA2, SPTBN1, DDR2, PLIN4, HSP90AA1, CRYAB, SORBS1
## ITGB1, AKAP12, GOLGA4, CES1, CCDC80, CCND1, MAP1B, ITIH5, PALMD, TNS1
## KIF5B, CAVIN1, UTRN, PTPN11, NCL, ITSN1, GPAM, KTN1, SCD, SEPT11
## IC_ 3
## Positive: AHNAK, DDR2, AKAP12, CAVIN1, TNS1, SPTBN1, FASN, ITGB1, MAP1B, KTN1
## HSP90AB1, LEP, ADH1B, DPT, HSP90AA1, MTURN, HMGB1, SEC62, GSN, TXNIP
## RTN4RL1, EIF3A, EIF5B, SORBS1, GOLGA4, SCD, HSP90B1, WDR60, DMD, ROCK2
## Negative: SAA2, SAA1, RBP4, SAA4, HP, PLA2G2A, FTL, FTH1, FABP4, COX6B1
## G0S2, UQCRQ, POLR2L, ATP6V1F, C4orf19, GPX4, COX7A2, COX6C, MGST1, OST4
## NMB, RCAN2, KRT14, NDUFB1, DEFB132, ATP5PO, MT1X, LY86, EEF2, TSPO
## IC_ 4
## Positive: SAA1, EGFL6, CES1, SAA2, ASAH1, TNMD, MEGF9, AKR1C1, PLIN4, AKR1C3
## AKR1C2, COL3A1, CACNA2D1, TMSB4X, ITIH5, NPY1R, PCK1, CLU, CD248, MCAM
## SEMA3C, PLIN2, SNX10, TUBB2A, ANXA5, SFRP2, TPM2, DGAT2, NPR3, STMN2
## Negative: TNS1, AKAP12, PABPC1, SORBS1, FASN, EEF1A1, NAP1L1, GPX3, ADIRF, EIF4B
## GLUL, ADH1B, PRKAR2B, ANP32B, FADS1, PTMA, FAU, TF, ST13, ITSN1
## UBA52, PFKFB3, NSA2, HLA-B, AHNAK, TOMM7, SPX, RACK1, GYG2, DCN
## IC_ 5
## Positive: SCD, SAA1, GPAM, FASN, DGAT2, PLIN4, PLIN1, ACSL1, SAA2, RBP4
## TF, ACACB, AGPAT2, XIST, TRDN, IGF1, ADIPOQ, CES1, MGLL, CEBPA
## ELOVL5, SLC16A7, PCK1, FZD4, ELOVL6, ELMOD3, THRSP, G0S2, GPD1, PTPRF
## Negative: FTH1, MAP1B, DPT, TMSB4X, FTL, CAVIN1, ITGB1, SPTBN1, MAP4, EGFL6
## DDR2, CSDE1, MTURN, EEF1A1, EIF1AY, AHNAK, CCDC80, SYNPO, S100A6, CLSTN2
## PTPN11, TMSB10, CRYAB, AKAP12, YBX1, S100A10, LEP, PTMA, PLAC9, SORBS1# Harmony
var_int <- c("date_exp", "subject_id")
se_adi <- RunHarmony(object = se_adi, group.by.vars = var_int, assay.use="SCT", reduction = "ica", plot_convergence = TRUE)
red_use <- "harmony"
dims_use_adi <- 1:13
se_adi <- FindNeighbors(object = se_adi, verbose = T, reduction = red_use, dims = dims_use_adi)
for (res in 0.5) {
print(res)
se_adi <- FindClusters(object = se_adi, verbose = T, algorithm = 1, resolution = res)
}## [1] 0.5
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 6655
## Number of edges: 175082
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.6537
## Number of communities: 5
## Elapsed time: 0 secondsres_use <- "SCT_snn_res.0.5"
se_adi[["seurat_clusters"]] <- se_adi[[res_use]]
se_adi[["seurat_clusters"]] <- as.factor(as.numeric(as.character(se_adi[[]]$seurat_clusters))+1)
se_adi <- SetIdent(se_adi, value = "seurat_clusters")
n <- length( unique(as.character(se_adi@meta.data$seurat_clusters)) )
print(paste("Number of clusters:", n))## [1] "Number of clusters: 5"print("Cluster sizes (n spots per cluster):")## [1] "Cluster sizes (n spots per cluster):"cluster_count_adi <- se_adi[["seurat_clusters"]] %>%
group_by(seurat_clusters) %>%
dplyr::count() %>%
dplyr::arrange(desc(n))
cluster_count_adi$pct <- round(cluster_count_adi$n / sum(cluster_count_adi$n), digits = 3) * 100
cluster_count_adi## # A tibble: 5 x 3
## # Groups: seurat_clusters [5]
## seurat_clusters n pct
## <fct> <int> <dbl>
## 1 1 1886 28.3
## 2 2 1569 23.6
## 3 3 1496 22.5
## 4 4 1103 16.6
## 5 5 601 9Cluster marker genes
Cluster DEA
LogFC cut-off 0.15
logf_thr <- 0.15
cluster_calc_de <- unique(sort(as.numeric(as.character(subset(cluster_prop, n>3)$seurat_clusters))))
markers_seu_clusters <- list()
for( i in cluster_calc_de ) {
print(paste("Finding markers for cluster", i))
c <- paste0("cluster_", i)
markers_seu_clusters[[c]] <- FindMarkers(se, ident.1 = as.character(i), logfc.threshold = logf_thr)
}## [1] "Finding markers for cluster 1"
## [1] "Finding markers for cluster 2"
## [1] "Finding markers for cluster 3"
## [1] "Finding markers for cluster 4"
## [1] "Finding markers for cluster 5"
## [1] "Finding markers for cluster 6"
## [1] "Finding markers for cluster 7"
## [1] "Finding markers for cluster 8"
## [1] "Finding markers for cluster 9"
## [1] "Finding markers for cluster 10"
## [1] "Finding markers for cluster 11"
## [1] "Finding markers for cluster 12"
## [1] "Finding markers for cluster 13"
## [1] "Finding markers for cluster 14"
## [1] "Finding markers for cluster 15"
## [1] "Finding markers for cluster 16"
## [1] "Finding markers for cluster 17"
## [1] "Finding markers for cluster 18"
## [1] "Finding markers for cluster 19"
## [1] "Finding markers for cluster 20"top_n_genes <- 10
logf_thr <- 0.15
se_markers <- FindAllMarkers(se,
only.pos = TRUE,
min.pct = 0.1,
logfc.threshold = logf_thr
)
se_markers$pct.diff <- se_markers$pct.1 - se_markers$pct.2
se_markers_top <- se_markers %>%
group_by(as.numeric(cluster)) %>%
dplyr::top_n(n = top_n_genes, wt = avg_logFC)
se_markers_top <- se_markers_top[, c("gene", "cluster", "avg_logFC", "pct.1", "pct.2", "pct.diff", "p_val", "p_val_adj")]
datatable(se_markers_top, rownames = F, caption = paste("Top", top_n_genes, "marker genes for the computed Seurat clusters"))fname <- paste0(ANALYSIS_ID, ".markers_clusterdea.xlsx")
sheets <- list()
sheets["top_markers_all_clusters"] <- list(as.data.frame(se_markers_top))
for (i in 1:length(markers_seu_clusters)){
c_name <- names(markers_seu_clusters[i])
c_data <- markers_seu_clusters[[i]]
c_names <- colnames(c_data)
c_data$gene <- rownames(c_data)
c_data <- c_data[, c("gene", c_names)]
sheets[c_name] <- list(c_data)
}
write_xlsx(
x = sheets,
path = file.path(DIR_RES, "tables", fname),
col_names = TRUE,
format_headers = TRUE
)ALL genes (no cut-off)
logf_thr <- 0
cluster_calc_de <- unique(sort(as.numeric(as.character(subset(cluster_prop, n>3)$seurat_clusters))))
markers_seu_clusters_all <- list()
for( i in cluster_calc_de ) {
print(paste("Finding markers for cluster", i))
c <- paste0("cluster_", i)
markers_seu_clusters_all[[c]] <- FindMarkers(se, ident.1 = as.character(i), logfc.threshold = logf_thr)
}## [1] "Finding markers for cluster 1"
## [1] "Finding markers for cluster 2"
## [1] "Finding markers for cluster 3"
## [1] "Finding markers for cluster 4"
## [1] "Finding markers for cluster 5"
## [1] "Finding markers for cluster 6"
## [1] "Finding markers for cluster 7"
## [1] "Finding markers for cluster 8"
## [1] "Finding markers for cluster 9"
## [1] "Finding markers for cluster 10"
## [1] "Finding markers for cluster 11"
## [1] "Finding markers for cluster 12"
## [1] "Finding markers for cluster 13"
## [1] "Finding markers for cluster 14"
## [1] "Finding markers for cluster 15"
## [1] "Finding markers for cluster 16"
## [1] "Finding markers for cluster 17"
## [1] "Finding markers for cluster 18"
## [1] "Finding markers for cluster 19"
## [1] "Finding markers for cluster 20"fname <- paste0(ANALYSIS_ID, ".markers_clusterdea_ALL.xlsx")
sheets <- list()
for (i in 1:length(markers_seu_clusters_all)){
c_name <- names(markers_seu_clusters_all[i])
c_data <- markers_seu_clusters_all[[i]]
c_names <- colnames(c_data)
c_data$gene <- rownames(c_data)
c_data <- c_data[, c("gene", c_names)]
sheets[c_name] <- list(c_data)
}
write_xlsx(
x = sheets,
path = file.path(DIR_RES, "tables", fname),
col_names = TRUE,
format_headers = TRUE
)Cluster DEA of adipocyte re-clustering
logf_thr <- 0
cluster_calc_de_adi <- sort(as.numeric(as.character(subset(cluster_count_adi, n>3)$seurat_clusters)))
markers_seu_clusters_adi <- list()
for( i in cluster_calc_de_adi ) {
print(paste("Finding markers for cluster", i))
c <- paste0("cluster_", i)
markers_seu_clusters_adi[[c]] <- FindMarkers(se_adi, ident.1 = as.character(i), logfc.threshold = logf_thr)
}## [1] "Finding markers for cluster 1"
## [1] "Finding markers for cluster 2"
## [1] "Finding markers for cluster 3"
## [1] "Finding markers for cluster 4"
## [1] "Finding markers for cluster 5"top_n_genes <- 10
logf_thr <- 0.15
se_markers_adi <- FindAllMarkers(se_adi, only.pos = TRUE, min.pct = 0.1, logfc.threshold = logf_thr)
se_markers_adi$pct.diff <- se_markers_adi$pct.1 - se_markers_adi$pct.2
se_markers_adi_top <- se_markers_adi %>%
group_by(cluster) %>%
dplyr::top_n(n = top_n_genes, wt = avg_logFC)
se_markers_adi_top <- se_markers_adi_top[, c("gene", "cluster", "avg_logFC", "pct.1", "pct.2", "pct.diff", "p_val", "p_val_adj")]
datatable(se_markers_adi_top, rownames = F, caption = paste("Top", top_n_genes, "marker genes for the computed Seurat clusters"))fname <- paste0(ANALYSIS_ID, "_adipocytes-reclust.markers_clusterdea.xlsx")
sheets <- list()
sheets["top_markers_all_clusters"] <- list(as.data.frame(se_markers_adi_top))
for (i in 1:length(markers_seu_clusters_adi)){
c_name <- names(markers_seu_clusters_adi[i])
c_data <- markers_seu_clusters_adi[[i]]
c_names <- colnames(c_data)
c_data$gene <- rownames(c_data)
c_data <- c_data[, c("gene", c_names)]
sheets[c_name] <- list(c_data)
}
write_xlsx(
x = sheets,
path = file.path(DIR_RES, "tables", fname),
col_names = TRUE,
format_headers = TRUE
)Pathway enrichment
Marker gene pathways enrichment analysis for each cluster.
pw_results_go <- list()
pw_results_ra <- list()
for(c in names(markers_seu_clusters)){ # names(markers_seu_clusters)
d <- NULL
d <- markers_seu_clusters[[c]]
d <- subset(d, avg_logFC>0 & p_val_adj<0.05)
d$gene_name <- rownames(d)
eg = clusterProfiler::bitr(d$gene_name, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
rownames(eg) <- eg$SYMBOL
colnames(eg) <- c("gene_name", "gene_entrez")
d <- merge(d, eg, by = "gene_name", all = T)
rownames(d) <- d$gene_name
print(c)
print(d$gene_name)
print("---------------------------------")
if( dim(d)[1] > 0 ) {
pw_genes <- d$gene_entrez[!is.na(d$gene_entrez)]
pw <- clusterProfiler::enrichGO(pw_genes, OrgDb="org.Hs.eg.db")
pw_ra <- enrichPathway(pw_genes)
pw_results_go[[c]] <- pw
pw_results_ra[[c]] <- pw_ra
if(!is.null(pw)) {
print("GO Enrichment: ")
print(subset(pw@result, p.adjust<0.05))
print("---------------------------------")
} else {
print("No GO Enrichment available for gene set.")
}
if(!is.null(pw_ra)) {
print("Reactome Enrichment: ")
print(subset(pw_ra@result, p.adjust<0.05))
print("---------------------------------")
} else {
print("No Reactome Enrichment available for gene set.")
}
} else {
print("Not enough genes to perform analysis --- Skipping cluster!")
}
print("==================================")
}## Loading required package: org.Hs.eg.db## Loading required package: AnnotationDbi## ## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_1"
## [1] "ADIRF"
## [1] "---------------------------------"## No gene set have size > 10 ...## --> return NULL...## [1] "No GO Enrichment available for gene set."
## [1] "Reactome Enrichment: "
## ID
## R-HSA-381340 R-HSA-381340
## Description
## R-HSA-381340 Transcriptional regulation of white adipocyte differentiation
## GeneRatio BgRatio pvalue p.adjust qvalue geneID Count
## R-HSA-381340 1/1 84/10654 0.007884363 0.007884363 NA 10974 1
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_2"
## [1] "AHNAK" "DST" "ITGB1" "SPTBN1"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID Description
## GO:0050839 GO:0050839 cell adhesion molecule binding
## GO:0045296 GO:0045296 cadherin binding
## GO:0003779 GO:0003779 actin binding
## GO:0005178 GO:0005178 integrin binding
## GO:0097493 GO:0097493 structural molecule activity conferring elasticity
## GO:0044548 GO:0044548 S100 protein binding
## GO:0051010 GO:0051010 microtubule plus-end binding
## GO:0030506 GO:0030506 ankyrin binding
## GO:0001968 GO:0001968 fibronectin binding
## GO:0043236 GO:0043236 laminin binding
## GO:0019956 GO:0019956 chemokine binding
## GO:0015026 GO:0015026 coreceptor activity
## GO:0050840 GO:0050840 extracellular matrix binding
## GO:0098631 GO:0098631 cell adhesion mediator activity
## GO:0005518 GO:0005518 collagen binding
## GO:0001618 GO:0001618 virus receptor activity
## GO:0104005 GO:0104005 hijacked molecular function
## GO:0005200 GO:0005200 structural constituent of cytoskeleton
## GO:0002020 GO:0002020 protease binding
## GO:0019955 GO:0019955 cytokine binding
## GO:0008022 GO:0008022 protein C-terminus binding
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0050839 4/4 499/17697 6.247649e-07 1.561912e-05 1.972942e-06
## GO:0045296 3/4 331/17697 2.557950e-05 3.197438e-04 4.038869e-05
## GO:0003779 3/4 431/17697 5.634903e-05 4.695753e-04 5.931477e-05
## GO:0005178 2/4 132/17697 3.280638e-04 2.050399e-03 2.589977e-04
## GO:0097493 1/4 11/17697 2.484190e-03 1.242095e-02 1.568962e-03
## GO:0044548 1/4 15/17697 3.386384e-03 1.370447e-02 1.731091e-03
## GO:0051010 1/4 17/17697 3.837251e-03 1.370447e-02 1.731091e-03
## GO:0030506 1/4 20/17697 4.513265e-03 1.410395e-02 1.781552e-03
## GO:0001968 1/4 27/17697 6.089292e-03 1.634810e-02 2.065024e-03
## GO:0043236 1/4 29/17697 6.539242e-03 1.634810e-02 2.065024e-03
## GO:0019956 1/4 32/17697 7.213880e-03 1.639518e-02 2.070970e-03
## GO:0015026 1/4 44/17697 9.908997e-03 2.064374e-02 2.607631e-03
## GO:0050840 1/4 57/17697 1.282251e-02 2.369674e-02 2.993272e-03
## GO:0098631 1/4 59/17697 1.327017e-02 2.369674e-02 2.993272e-03
## GO:0005518 1/4 67/17697 1.505930e-02 2.444527e-02 3.087823e-03
## GO:0001618 1/4 74/17697 1.662278e-02 2.444527e-02 3.087823e-03
## GO:0104005 1/4 74/17697 1.662278e-02 2.444527e-02 3.087823e-03
## GO:0005200 1/4 102/17697 2.285812e-02 3.174739e-02 4.010196e-03
## GO:0002020 1/4 128/17697 2.862148e-02 3.577685e-02 4.519181e-03
## GO:0019955 1/4 128/17697 2.862148e-02 3.577685e-02 4.519181e-03
## GO:0008022 1/4 187/17697 4.160529e-02 4.953011e-02 6.256435e-03
## geneID Count
## GO:0050839 79026/667/3688/6711 4
## GO:0045296 79026/3688/6711 3
## GO:0003779 667/3688/6711 3
## GO:0005178 667/3688 2
## GO:0097493 79026 1
## GO:0044548 79026 1
## GO:0051010 667 1
## GO:0030506 6711 1
## GO:0001968 3688 1
## GO:0043236 3688 1
## GO:0019956 3688 1
## GO:0015026 3688 1
## GO:0050840 3688 1
## GO:0098631 3688 1
## GO:0005518 3688 1
## GO:0001618 3688 1
## GO:0104005 3688 1
## GO:0005200 6711 1
## GO:0002020 3688 1
## GO:0019955 3688 1
## GO:0008022 667 1
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-1500931 R-HSA-1500931
## R-HSA-446728 R-HSA-446728
## R-HSA-373760 R-HSA-373760
## R-HSA-1474244 R-HSA-1474244
## R-HSA-446107 R-HSA-446107
## R-HSA-75892 R-HSA-75892
## R-HSA-446353 R-HSA-446353
## R-HSA-416700 R-HSA-416700
## R-HSA-445144 R-HSA-445144
## R-HSA-373753 R-HSA-373753
## R-HSA-210991 R-HSA-210991
## R-HSA-3000170 R-HSA-3000170
## R-HSA-3000157 R-HSA-3000157
## R-HSA-8874081 R-HSA-8874081
## R-HSA-445095 R-HSA-445095
## R-HSA-2129379 R-HSA-2129379
## R-HSA-8875878 R-HSA-8875878
## R-HSA-1566948 R-HSA-1566948
## R-HSA-3000171 R-HSA-3000171
## R-HSA-2022090 R-HSA-2022090
## R-HSA-375165 R-HSA-375165
## R-HSA-373755 R-HSA-373755
## R-HSA-3000178 R-HSA-3000178
## R-HSA-6806834 R-HSA-6806834
## R-HSA-216083 R-HSA-216083
## R-HSA-1474290 R-HSA-1474290
## R-HSA-6807878 R-HSA-6807878
## R-HSA-6785807 R-HSA-6785807
## Description
## R-HSA-1500931 Cell-Cell communication
## R-HSA-446728 Cell junction organization
## R-HSA-373760 L1CAM interactions
## R-HSA-1474244 Extracellular matrix organization
## R-HSA-446107 Type I hemidesmosome assembly
## R-HSA-75892 Platelet Adhesion to exposed collagen
## R-HSA-446353 Cell-extracellular matrix interactions
## R-HSA-416700 Other semaphorin interactions
## R-HSA-445144 Signal transduction by L1
## R-HSA-373753 Nephrin family interactions
## R-HSA-210991 Basigin interactions
## R-HSA-3000170 Syndecan interactions
## R-HSA-3000157 Laminin interactions
## R-HSA-8874081 MET activates PTK2 signaling
## R-HSA-445095 Interaction between L1 and Ankyrins
## R-HSA-2129379 Molecules associated with elastic fibres
## R-HSA-8875878 MET promotes cell motility
## R-HSA-1566948 Elastic fibre formation
## R-HSA-3000171 Non-integrin membrane-ECM interactions
## R-HSA-2022090 Assembly of collagen fibrils and other multimeric structures
## R-HSA-375165 NCAM signaling for neurite out-growth
## R-HSA-373755 Semaphorin interactions
## R-HSA-3000178 ECM proteoglycans
## R-HSA-6806834 Signaling by MET
## R-HSA-216083 Integrin cell surface interactions
## R-HSA-1474290 Collagen formation
## R-HSA-6807878 COPI-mediated anterograde transport
## R-HSA-6785807 Interleukin-4 and Interleukin-13 signaling
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-1500931 3/3 129/10654 1.734553e-06 7.285123e-05 1.825845e-05
## R-HSA-446728 2/3 91/10654 2.152755e-04 4.520786e-03 1.133029e-03
## R-HSA-373760 2/3 119/10654 3.684459e-04 5.158243e-03 1.292793e-03
## R-HSA-1474244 2/3 301/10654 2.342180e-03 2.437276e-02 6.108460e-03
## R-HSA-446107 1/3 11/10654 3.094521e-03 2.437276e-02 6.108460e-03
## R-HSA-75892 1/3 15/10654 4.218217e-03 2.437276e-02 6.108460e-03
## R-HSA-446353 1/3 18/10654 5.060435e-03 2.437276e-02 6.108460e-03
## R-HSA-416700 1/3 19/10654 5.341068e-03 2.437276e-02 6.108460e-03
## R-HSA-445144 1/3 21/10654 5.902177e-03 2.437276e-02 6.108460e-03
## R-HSA-373753 1/3 23/10654 6.463075e-03 2.437276e-02 6.108460e-03
## R-HSA-210991 1/3 25/10654 7.023762e-03 2.437276e-02 6.108460e-03
## R-HSA-3000170 1/3 27/10654 7.584237e-03 2.437276e-02 6.108460e-03
## R-HSA-3000157 1/3 30/10654 8.424555e-03 2.437276e-02 6.108460e-03
## R-HSA-8874081 1/3 30/10654 8.424555e-03 2.437276e-02 6.108460e-03
## R-HSA-445095 1/3 31/10654 8.704556e-03 2.437276e-02 6.108460e-03
## R-HSA-2129379 1/3 38/10654 1.066308e-02 2.799060e-02 7.015187e-03
## R-HSA-8875878 1/3 41/10654 1.150166e-02 2.841587e-02 7.121773e-03
## R-HSA-1566948 1/3 45/10654 1.261903e-02 2.944441e-02 7.379551e-03
## R-HSA-3000171 1/3 59/10654 1.652319e-02 3.473255e-02 8.704901e-03
## R-HSA-2022090 1/3 61/10654 1.708008e-02 3.473255e-02 8.704901e-03
## R-HSA-375165 1/3 63/10654 1.763677e-02 3.473255e-02 8.704901e-03
## R-HSA-373755 1/3 65/10654 1.819324e-02 3.473255e-02 8.704901e-03
## R-HSA-3000178 1/3 76/10654 2.125010e-02 3.864469e-02 9.685387e-03
## R-HSA-6806834 1/3 79/10654 2.208268e-02 3.864469e-02 9.685387e-03
## R-HSA-216083 1/3 85/10654 2.374644e-02 3.989401e-02 9.998499e-03
## R-HSA-1474290 1/3 90/10654 2.513145e-02 4.059696e-02 1.017468e-02
## R-HSA-6807878 1/3 101/10654 2.817388e-02 4.382604e-02 1.098397e-02
## R-HSA-6785807 1/3 108/10654 3.010667e-02 4.516001e-02 1.131830e-02
## geneID Count
## R-HSA-1500931 667/3688/6711 3
## R-HSA-446728 667/3688 2
## R-HSA-373760 3688/6711 2
## R-HSA-1474244 667/3688 2
## R-HSA-446107 667 1
## R-HSA-75892 3688 1
## R-HSA-446353 3688 1
## R-HSA-416700 3688 1
## R-HSA-445144 3688 1
## R-HSA-373753 6711 1
## R-HSA-210991 3688 1
## R-HSA-3000170 3688 1
## R-HSA-3000157 3688 1
## R-HSA-8874081 3688 1
## R-HSA-445095 6711 1
## R-HSA-2129379 3688 1
## R-HSA-8875878 3688 1
## R-HSA-1566948 3688 1
## R-HSA-3000171 3688 1
## R-HSA-2022090 667 1
## R-HSA-375165 6711 1
## R-HSA-373755 3688 1
## R-HSA-3000178 3688 1
## R-HSA-6806834 3688 1
## R-HSA-216083 3688 1
## R-HSA-1474290 667 1
## R-HSA-6807878 6711 1
## R-HSA-6785807 3688 1
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_3"
## [1] "CAVIN1" "CCND1" "CRTAP" "CSDE1" "DDR2" "FTH1" "FTL" "HSPB6"
## [9] "LEP" "MAP4" "MTURN" "PABPC1" "PTMA" "PTPN11" "SEC62" "SORBS1"
## [17] "SPTBN1" "TNS1"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID
## GO:0004322 GO:0004322
## GO:0016724 GO:0016724
## GO:0008199 GO:0008199
## GO:0016722 GO:0016722
## GO:0051428 GO:0051428
## GO:0005158 GO:0005158
## GO:0008198 GO:0008198
## GO:0005070 GO:0005070
## GO:0035591 GO:0035591
## GO:0003779 GO:0003779
## Description
## GO:0004322 ferroxidase activity
## GO:0016724 oxidoreductase activity, oxidizing metal ions, oxygen as acceptor
## GO:0008199 ferric iron binding
## GO:0016722 oxidoreductase activity, oxidizing metal ions
## GO:0051428 peptide hormone receptor binding
## GO:0005158 insulin receptor binding
## GO:0008198 ferrous iron binding
## GO:0005070 SH3/SH2 adaptor activity
## GO:0035591 signaling adaptor activity
## GO:0003779 actin binding
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0004322 2/17 10/17697 3.890835e-05 0.0009980834 0.0005669981
## GO:0016724 2/17 10/17697 3.890835e-05 0.0009980834 0.0005669981
## GO:0008199 2/17 11/17697 4.752778e-05 0.0009980834 0.0005669981
## GO:0016722 2/17 19/17697 1.471017e-04 0.0020829876 0.0011833180
## GO:0051428 2/17 21/17697 1.804472e-04 0.0020829876 0.0011833180
## GO:0005158 2/17 22/17697 1.983798e-04 0.0020829876 0.0011833180
## GO:0008198 2/17 24/17697 2.367575e-04 0.0021308177 0.0012104896
## GO:0005070 2/17 52/17697 1.119621e-03 0.0088170173 0.0050088319
## GO:0035591 2/17 80/17697 2.626354e-03 0.0183844765 0.0104439800
## GO:0003779 3/17 431/17697 7.565746e-03 0.0476642024 0.0270774082
## geneID Count
## GO:0004322 2495/2512 2
## GO:0016724 2495/2512 2
## GO:0008199 2495/2512 2
## GO:0016722 2495/2512 2
## GO:0051428 3952/5781 2
## GO:0005158 5781/10580 2
## GO:0008198 2495/2512 2
## GO:0005070 5781/10580 2
## GO:0035591 5781/10580 2
## GO:0003779 10580/6711/7145 3
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-2586552 R-HSA-2586552
## R-HSA-8934593 R-HSA-8934593
## R-HSA-3000480 R-HSA-3000480
## R-HSA-2173782 R-HSA-2173782
## R-HSA-432722 R-HSA-432722
## R-HSA-917937 R-HSA-917937
## Description GeneRatio
## R-HSA-2586552 Signaling by Leptin 2/11
## R-HSA-8934593 Regulation of RUNX1 Expression and Activity 2/11
## R-HSA-3000480 Scavenging by Class A Receptors 2/11
## R-HSA-2173782 Binding and Uptake of Ligands by Scavenger Receptors 2/11
## R-HSA-432722 Golgi Associated Vesicle Biogenesis 2/11
## R-HSA-917937 Iron uptake and transport 2/11
## BgRatio pvalue p.adjust qvalue geneID Count
## R-HSA-2586552 11/10654 5.303572e-05 0.008167501 0.004689474 3952/5781 2
## R-HSA-8934593 17/10654 1.307003e-04 0.008426434 0.004838144 595/5781 2
## R-HSA-3000480 19/10654 1.641513e-04 0.008426434 0.004838144 2495/2512 2
## R-HSA-2173782 42/10654 8.158747e-04 0.031411174 0.018035124 2495/2512 2
## R-HSA-432722 56/10654 1.447823e-03 0.039842592 0.022876129 2495/2512 2
## R-HSA-917937 58/10654 1.552309e-03 0.039842592 0.022876129 2495/2512 2
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_4"
## [1] "ADH1B" "ADIPOQ" "AGPAT2" "AOC3" "CAV1" "CEBPA" "CIDEC" "CRYAB"
## [9] "DEPP1" "DGAT2" "EMP1" "FABP4" "FASN" "G0S2" "GPAM" "GPD1"
## [17] "LIPE" "LRP1" "MCAM" "MGLL" "MT1X" "PFKFB3" "PLIN1" "PLIN4"
## [25] "PNPLA2" "PTPRF" "RASD1" "RTN4" "TRARG1"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID
## GO:0008374 GO:0008374
## GO:0016747 GO:0016747
## GO:0004806 GO:0004806
## GO:0016411 GO:0016411
## GO:0016746 GO:0016746
## GO:0016616 GO:0016616
## GO:0016298 GO:0016298
## GO:0016614 GO:0016614
## GO:0052689 GO:0052689
## GO:0043394 GO:0043394
## Description
## GO:0008374 O-acyltransferase activity
## GO:0016747 transferase activity, transferring acyl groups other than amino-acyl groups
## GO:0004806 triglyceride lipase activity
## GO:0016411 acylglycerol O-acyltransferase activity
## GO:0016746 transferase activity, transferring acyl groups
## GO:0016616 oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor
## GO:0016298 lipase activity
## GO:0016614 oxidoreductase activity, acting on CH-OH group of donors
## GO:0052689 carboxylic ester hydrolase activity
## GO:0043394 proteoglycan binding
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0008374 3/25 47/17697 3.875695e-05 0.004340778 0.002774182
## GO:0016747 4/25 232/17697 2.932022e-04 0.010593125 0.006770042
## GO:0004806 2/25 21/17697 3.957735e-04 0.010593125 0.006770042
## GO:0016411 2/25 21/17697 3.957735e-04 0.010593125 0.006770042
## GO:0016746 4/25 264/17697 4.783924e-04 0.010593125 0.006770042
## GO:0016616 3/25 119/17697 6.120194e-04 0.010593125 0.006770042
## GO:0016298 3/25 127/17697 7.396059e-04 0.010593125 0.006770042
## GO:0016614 3/25 128/17697 7.566518e-04 0.010593125 0.006770042
## GO:0052689 3/25 136/17697 9.021019e-04 0.011226157 0.007174611
## GO:0043394 2/25 36/17697 1.172007e-03 0.013126480 0.008389104
## geneID Count
## GO:0008374 10555/84649/57678 3
## GO:0016747 10555/84649/2194/57678 4
## GO:0004806 3991/57104 2
## GO:0016411 10555/84649 2
## GO:0016746 10555/84649/2194/57678 4
## GO:0016616 125/2194/2819 3
## GO:0016298 3991/11343/57104 3
## GO:0016614 125/2194/2819 3
## GO:0052689 3991/11343/57104 3
## GO:0043394 4035/5792 2
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-8979227 R-HSA-8979227
## R-HSA-163560 R-HSA-163560
## R-HSA-1483206 R-HSA-1483206
## R-HSA-1483257 R-HSA-1483257
## R-HSA-381340 R-HSA-381340
## R-HSA-1483166 R-HSA-1483166
## R-HSA-75109 R-HSA-75109
## R-HSA-9006931 R-HSA-9006931
## R-HSA-2426168 R-HSA-2426168
## R-HSA-9024446 R-HSA-9024446
## R-HSA-1655829 R-HSA-1655829
## Description
## R-HSA-8979227 Triglyceride metabolism
## R-HSA-163560 Triglyceride catabolism
## R-HSA-1483206 Glycerophospholipid biosynthesis
## R-HSA-1483257 Phospholipid metabolism
## R-HSA-381340 Transcriptional regulation of white adipocyte differentiation
## R-HSA-1483166 Synthesis of PA
## R-HSA-75109 Triglyceride biosynthesis
## R-HSA-9006931 Signaling by Nuclear Receptors
## R-HSA-2426168 Activation of gene expression by SREBF (SREBP)
## R-HSA-9024446 NR1H2 and NR1H3-mediated signaling
## R-HSA-1655829 Regulation of cholesterol biosynthesis by SREBP (SREBF)
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-8979227 7/23 37/10654 7.863750e-13 5.740537e-11 3.807710e-11
## R-HSA-163560 5/23 24/10654 1.218406e-09 4.447182e-08 2.949825e-08
## R-HSA-1483206 6/23 129/10654 2.390810e-07 5.817638e-06 3.858852e-06
## R-HSA-1483257 6/23 212/10654 4.400125e-06 8.030228e-05 5.326467e-05
## R-HSA-381340 4/23 84/10654 2.840410e-05 4.146999e-04 2.750713e-04
## R-HSA-1483166 3/23 40/10654 8.242764e-05 1.002870e-03 6.652055e-04
## R-HSA-75109 2/23 13/10654 3.427525e-04 3.574418e-03 2.370919e-03
## R-HSA-9006931 4/23 299/10654 3.531139e-03 2.954235e-02 1.959550e-02
## R-HSA-2426168 2/23 42/10654 3.642207e-03 2.954235e-02 1.959550e-02
## R-HSA-9024446 2/23 47/10654 4.543001e-03 3.316391e-02 2.199769e-02
## R-HSA-1655829 2/23 55/10654 6.175833e-03 4.098508e-02 2.718549e-02
## geneID Count
## R-HSA-8979227 857/84649/2167/57678/3991/11343/5346 7
## R-HSA-163560 857/2167/3991/11343/5346 5
## R-HSA-1483206 10555/84649/57678/2819/11343/57104 6
## R-HSA-1483257 10555/84649/57678/2819/11343/57104 6
## R-HSA-381340 9370/1050/2167/5346 4
## R-HSA-1483166 10555/57678/2819 3
## R-HSA-75109 84649/57678 2
## R-HSA-9006931 857/2194/57678/5346 4
## R-HSA-2426168 2194/57678 2
## R-HSA-9024446 2194/5346 2
## R-HSA-1655829 2194/57678 2
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_5"
## [1] "A2M" "ADGRF5" "ADGRL4" "AQP1" "B2M" "BST2" "BTNL9"
## [8] "CAVIN2" "CD300LG" "CD74" "CLDN5" "COL4A1" "EPAS1" "GNG11"
## [15] "HLA-A" "HLA-B" "HLA-C" "HLA-E" "IFI27" "IGFBP4" "IGFBP7"
## [22] "MYH9" "PECAM1" "POSTN" "RBP7" "SPARCL1" "TCF4" "THBS4"
## [29] "TIMP3" "TXNIP" "UACA" "VWF"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID Description GeneRatio
## GO:0042605 GO:0042605 peptide antigen binding 4/32
## GO:0019838 GO:0019838 growth factor binding 4/32
## GO:0042277 GO:0042277 peptide binding 5/32
## GO:0003823 GO:0003823 antigen binding 4/32
## GO:0005201 GO:0005201 extracellular matrix structural constituent 4/32
## GO:0008191 GO:0008191 metalloendopeptidase inhibitor activity 2/32
## GO:0033218 GO:0033218 amide binding 5/32
## GO:0005520 GO:0005520 insulin-like growth factor binding 2/32
## GO:0002020 GO:0002020 protease binding 3/32
## GO:0005178 GO:0005178 integrin binding 3/32
## GO:0042287 GO:0042287 MHC protein binding 2/32
## GO:0004866 GO:0004866 endopeptidase inhibitor activity 3/32
## GO:0030414 GO:0030414 peptidase inhibitor activity 3/32
## GO:0061135 GO:0061135 endopeptidase regulator activity 3/32
## GO:0004857 GO:0004857 enzyme inhibitor activity 4/32
## GO:0005518 GO:0005518 collagen binding 2/32
## GO:0061134 GO:0061134 peptidase regulator activity 3/32
## BgRatio pvalue p.adjust qvalue
## GO:0042605 31/17697 2.676416e-07 2.729944e-05 1.690368e-05
## GO:0019838 137/17697 1.044539e-04 4.161176e-03 2.576580e-03
## GO:0042277 295/17697 1.731991e-04 4.161176e-03 2.576580e-03
## GO:0003823 160/17697 1.899590e-04 4.161176e-03 2.576580e-03
## GO:0005201 163/17697 2.039792e-04 4.161176e-03 2.576580e-03
## GO:0008191 16/17697 3.741507e-04 6.010577e-03 3.721719e-03
## GO:0033218 356/17697 4.124906e-04 6.010577e-03 3.721719e-03
## GO:0005520 28/17697 1.162719e-03 1.482467e-02 9.179362e-03
## GO:0002020 128/17697 1.572633e-03 1.751886e-02 1.084759e-02
## GO:0005178 132/17697 1.717536e-03 1.751886e-02 1.084759e-02
## GO:0042287 40/17697 2.367023e-03 2.194876e-02 1.359056e-02
## GO:0004866 175/17697 3.818719e-03 3.035573e-02 1.879612e-02
## GO:0030414 182/17697 4.261807e-03 3.035573e-02 1.879612e-02
## GO:0061135 182/17697 4.261807e-03 3.035573e-02 1.879612e-02
## GO:0004857 375/17697 4.464079e-03 3.035573e-02 1.879612e-02
## GO:0005518 67/17697 6.508842e-03 4.149387e-02 2.569280e-02
## GO:0061134 219/17697 7.117461e-03 4.270477e-02 2.644258e-02
## geneID Count
## GO:0042605 3105/3106/3107/3133 4
## GO:0019838 2/1282/3487/3490 4
## GO:0042277 972/3105/3106/3107/3133 5
## GO:0003823 3105/3106/3107/3133 4
## GO:0005201 1282/3490/10631/7450 4
## GO:0008191 684/7078 2
## GO:0033218 972/3105/3106/3107/3133 5
## GO:0005520 3487/3490 2
## GO:0002020 2/7078/7450 3
## GO:0005178 4627/7060/7450 3
## GO:0042287 972/3133 2
## GO:0004866 2/684/7078 3
## GO:0030414 2/684/7078 3
## GO:0061135 2/684/7078 3
## GO:0004857 2/684/7078/10628 4
## GO:0005518 8404/7450 2
## GO:0061134 2/684/7078 3
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-1236977 R-HSA-1236977
## R-HSA-983170 R-HSA-983170
## R-HSA-909733 R-HSA-909733
## R-HSA-913531 R-HSA-913531
## R-HSA-198933 R-HSA-198933
## R-HSA-1236974 R-HSA-1236974
## R-HSA-877300 R-HSA-877300
## R-HSA-1236975 R-HSA-1236975
## R-HSA-114608 R-HSA-114608
## R-HSA-76005 R-HSA-76005
## R-HSA-76002 R-HSA-76002
## R-HSA-216083 R-HSA-216083
## R-HSA-140837 R-HSA-140837
## R-HSA-983169 R-HSA-983169
## R-HSA-8957275 R-HSA-8957275
## R-HSA-2424491 R-HSA-2424491
## R-HSA-381426 R-HSA-381426
## R-HSA-140877 R-HSA-140877
## R-HSA-2172127 R-HSA-2172127
## R-HSA-432040 R-HSA-432040
## Description
## R-HSA-1236977 Endosomal/Vacuolar pathway
## R-HSA-983170 Antigen Presentation: Folding, assembly and peptide loading of class I MHC
## R-HSA-909733 Interferon alpha/beta signaling
## R-HSA-913531 Interferon Signaling
## R-HSA-198933 Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell
## R-HSA-1236974 ER-Phagosome pathway
## R-HSA-877300 Interferon gamma signaling
## R-HSA-1236975 Antigen processing-Cross presentation
## R-HSA-114608 Platelet degranulation
## R-HSA-76005 Response to elevated platelet cytosolic Ca2+
## R-HSA-76002 Platelet activation, signaling and aggregation
## R-HSA-216083 Integrin cell surface interactions
## R-HSA-140837 Intrinsic Pathway of Fibrin Clot Formation
## R-HSA-983169 Class I MHC mediated antigen processing & presentation
## R-HSA-8957275 Post-translational protein phosphorylation
## R-HSA-2424491 DAP12 signaling
## R-HSA-381426 Regulation of Insulin-like Growth Factor (IGF) transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs)
## R-HSA-140877 Formation of Fibrin Clot (Clotting Cascade)
## R-HSA-2172127 DAP12 interactions
## R-HSA-432040 Vasopressin regulates renal water homeostasis via Aquaporins
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-1236977 5/28 11/10654 3.930412e-11 6.406571e-09 4.716494e-09
## R-HSA-983170 5/28 25/10654 4.407362e-09 3.592000e-07 2.644417e-07
## R-HSA-909733 6/28 69/10654 1.991051e-08 1.081804e-06 7.964203e-07
## R-HSA-913531 7/28 199/10654 6.059964e-07 2.469435e-05 1.817989e-05
## R-HSA-198933 6/28 132/10654 9.720846e-07 3.168996e-05 2.333003e-05
## R-HSA-1236974 5/28 83/10654 2.169262e-06 5.893161e-05 4.338524e-05
## R-HSA-877300 5/28 92/10654 3.614972e-06 8.417720e-05 6.197095e-05
## R-HSA-1236975 5/28 99/10654 5.191562e-06 1.057781e-04 7.787344e-05
## R-HSA-114608 4/28 129/10654 3.352958e-04 6.072579e-03 4.470611e-03
## R-HSA-76005 4/28 134/10654 3.875588e-04 6.317208e-03 4.650705e-03
## R-HSA-76002 5/28 262/10654 5.350467e-04 7.928420e-03 5.836873e-03
## R-HSA-216083 3/28 85/10654 1.390180e-03 1.867523e-02 1.374863e-02
## R-HSA-140837 2/28 22/10654 1.489435e-03 1.867523e-02 1.374863e-02
## R-HSA-983169 5/28 371/10654 2.527845e-03 2.820501e-02 2.076443e-02
## R-HSA-8957275 3/28 108/10654 2.759841e-03 2.820501e-02 2.076443e-02
## R-HSA-2424491 2/28 30/10654 2.768590e-03 2.820501e-02 2.076443e-02
## R-HSA-381426 3/28 125/10654 4.169348e-03 3.997669e-02 2.943069e-02
## R-HSA-140877 2/28 39/10654 4.647825e-03 4.208863e-02 3.098550e-02
## R-HSA-2172127 2/28 43/10654 5.627364e-03 4.586301e-02 3.376418e-02
## R-HSA-432040 2/28 43/10654 5.627364e-03 4.586301e-02 3.376418e-02
## geneID Count
## R-HSA-1236977 567/3105/3106/3107/3133 5
## R-HSA-983170 567/3105/3106/3107/3133 5
## R-HSA-909733 684/3105/3106/3107/3133/3429 6
## R-HSA-913531 567/684/3105/3106/3107/3133/3429 7
## R-HSA-198933 567/146894/3105/3106/3107/3133 6
## R-HSA-1236974 567/3105/3106/3107/3133 5
## R-HSA-877300 567/3105/3106/3107/3133 5
## R-HSA-1236975 567/3105/3106/3107/3133 5
## R-HSA-114608 2/5175/7078/7450 4
## R-HSA-76005 2/5175/7078/7450 4
## R-HSA-76002 2/2791/5175/7078/7450 5
## R-HSA-216083 1282/5175/7450 3
## R-HSA-140837 2/7450 2
## R-HSA-983169 567/3105/3106/3107/3133 5
## R-HSA-8957275 3487/3490/8404 3
## R-HSA-2424491 567/3133 2
## R-HSA-381426 3487/3490/8404 3
## R-HSA-140877 2/7450 2
## R-HSA-2172127 567/3133 2
## R-HSA-432040 358/2791 2
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_6"
## [1] "CES1" "DGAT2" "FTL" "G0S2" "GPD1" "LEP" "LPL" "MGST1" "PLIN4"
## [10] "RBP4" "SAA1" "SAA2"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID
## GO:0004806 GO:0004806
## GO:0042056 GO:0042056
## GO:1901681 GO:1901681
## GO:0016298 GO:0016298
## GO:0048018 GO:0048018
## GO:0052689 GO:0052689
## GO:0008201 GO:0008201
## GO:0004322 GO:0004322
## GO:0016724 GO:0016724
## GO:0008199 GO:0008199
## GO:0043295 GO:0043295
## GO:1900750 GO:1900750
## GO:0043495 GO:0043495
## GO:0005539 GO:0005539
## GO:0019841 GO:0019841
## GO:0016918 GO:0016918
## GO:0043395 GO:0043395
## GO:0034185 GO:0034185
## GO:0016722 GO:0016722
## GO:0004602 GO:0004602
## GO:0016411 GO:0016411
## GO:0051428 GO:0051428
## GO:0008198 GO:0008198
## GO:0004364 GO:0004364
## GO:0090482 GO:0090482
## GO:0071813 GO:0071813
## GO:0071814 GO:0071814
## GO:0005501 GO:0005501
## GO:0019840 GO:0019840
## GO:0043394 GO:0043394
## GO:0019213 GO:0019213
## GO:1901618 GO:1901618
## GO:0008374 GO:0008374
## Description
## GO:0004806 triglyceride lipase activity
## GO:0042056 chemoattractant activity
## GO:1901681 sulfur compound binding
## GO:0016298 lipase activity
## GO:0048018 receptor ligand activity
## GO:0052689 carboxylic ester hydrolase activity
## GO:0008201 heparin binding
## GO:0004322 ferroxidase activity
## GO:0016724 oxidoreductase activity, oxidizing metal ions, oxygen as acceptor
## GO:0008199 ferric iron binding
## GO:0043295 glutathione binding
## GO:1900750 oligopeptide binding
## GO:0043495 protein membrane anchor
## GO:0005539 glycosaminoglycan binding
## GO:0019841 retinol binding
## GO:0016918 retinal binding
## GO:0043395 heparan sulfate proteoglycan binding
## GO:0034185 apolipoprotein binding
## GO:0016722 oxidoreductase activity, oxidizing metal ions
## GO:0004602 glutathione peroxidase activity
## GO:0016411 acylglycerol O-acyltransferase activity
## GO:0051428 peptide hormone receptor binding
## GO:0008198 ferrous iron binding
## GO:0004364 glutathione transferase activity
## GO:0090482 vitamin transmembrane transporter activity
## GO:0071813 lipoprotein particle binding
## GO:0071814 protein-lipid complex binding
## GO:0005501 retinoid binding
## GO:0019840 isoprenoid binding
## GO:0043394 proteoglycan binding
## GO:0019213 deacetylase activity
## GO:1901618 organic hydroxy compound transmembrane transporter activity
## GO:0008374 O-acyltransferase activity
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0004806 2/11 21/17697 0.0000732890 0.003957606 0.001311487
## GO:0042056 2/11 38/17697 0.0002439332 0.006586198 0.002182561
## GO:1901681 3/11 250/17697 0.0004226375 0.007607475 0.002520996
## GO:0016298 2/11 127/17697 0.0026937169 0.027731119 0.009189650
## GO:0048018 3/11 482/17697 0.0028142626 0.027731119 0.009189650
## GO:0052689 2/11 136/17697 0.0030812354 0.027731119 0.009189650
## GO:0008201 2/11 169/17697 0.0047118149 0.031457027 0.010424356
## GO:0004322 1/11 10/17697 0.0061999579 0.031457027 0.010424356
## GO:0016724 1/11 10/17697 0.0061999579 0.031457027 0.010424356
## GO:0008199 1/11 11/17697 0.0068180277 0.031457027 0.010424356
## GO:0043295 1/11 11/17697 0.0068180277 0.031457027 0.010424356
## GO:1900750 1/11 12/17697 0.0074357480 0.031457027 0.010424356
## GO:0043495 1/11 13/17697 0.0080531191 0.031457027 0.010424356
## GO:0005539 2/11 229/17697 0.0084903741 0.031457027 0.010424356
## GO:0019841 1/11 15/17697 0.0092868140 0.031457027 0.010424356
## GO:0016918 1/11 16/17697 0.0099031382 0.031457027 0.010424356
## GO:0043395 1/11 16/17697 0.0099031382 0.031457027 0.010424356
## GO:0034185 1/11 17/17697 0.0105191138 0.031557342 0.010457599
## GO:0016722 1/11 19/17697 0.0117500201 0.031858857 0.010557516
## GO:0004602 1/11 21/17697 0.0129795342 0.031858857 0.010557516
## GO:0016411 1/11 21/17697 0.0129795342 0.031858857 0.010557516
## GO:0051428 1/11 21/17697 0.0129795342 0.031858857 0.010557516
## GO:0008198 1/11 24/17697 0.0148211981 0.034797595 0.011531367
## GO:0004364 1/11 27/17697 0.0166597375 0.035985033 0.011924865
## GO:0090482 1/11 27/17697 0.0166597375 0.035985033 0.011924865
## GO:0071813 1/11 33/17697 0.0203274619 0.039881982 0.013216251
## GO:0071814 1/11 33/17697 0.0203274619 0.039881982 0.013216251
## GO:0005501 1/11 35/17697 0.0215472703 0.039881982 0.013216251
## GO:0019840 1/11 36/17697 0.0221566565 0.039881982 0.013216251
## GO:0043394 1/11 36/17697 0.0221566565 0.039881982 0.013216251
## GO:0019213 1/11 44/17697 0.0270193397 0.045595136 0.015109499
## GO:1901618 1/11 44/17697 0.0270193397 0.045595136 0.015109499
## GO:0008374 1/11 47/17697 0.0288371709 0.047188098 0.015637381
## geneID Count
## GO:0004806 1066/4023 2
## GO:0042056 6288/6289 2
## GO:1901681 4023/4257/6288 3
## GO:0016298 1066/4023 2
## GO:0048018 3952/6288/6289 3
## GO:0052689 1066/4023 2
## GO:0008201 4023/6288 2
## GO:0004322 2512 1
## GO:0016724 2512 1
## GO:0008199 2512 1
## GO:0043295 4257 1
## GO:1900750 4257 1
## GO:0043495 4023 1
## GO:0005539 4023/6288 2
## GO:0019841 5950 1
## GO:0016918 5950 1
## GO:0043395 4023 1
## GO:0034185 4023 1
## GO:0016722 2512 1
## GO:0004602 4257 1
## GO:0016411 84649 1
## GO:0051428 3952 1
## GO:0008198 2512 1
## GO:0004364 4257 1
## GO:0090482 5950 1
## GO:0071813 4023 1
## GO:0071814 4023 1
## GO:0005501 5950 1
## GO:0019840 5950 1
## GO:0043394 4023 1
## GO:0019213 1066 1
## GO:1901618 5950 1
## GO:0008374 84649 1
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-2173782 R-HSA-2173782
## R-HSA-975634 R-HSA-975634
## R-HSA-6806667 R-HSA-6806667
## R-HSA-381340 R-HSA-381340
## R-HSA-2980736 R-HSA-2980736
## R-HSA-2187338 R-HSA-2187338
## Description
## R-HSA-2173782 Binding and Uptake of Ligands by Scavenger Receptors
## R-HSA-975634 Retinoid metabolism and transport
## R-HSA-6806667 Metabolism of fat-soluble vitamins
## R-HSA-381340 Transcriptional regulation of white adipocyte differentiation
## R-HSA-2980736 Peptide hormone metabolism
## R-HSA-2187338 Visual phototransduction
## GeneRatio BgRatio pvalue p.adjust qvalue geneID
## R-HSA-2173782 2/10 42/10654 0.0006692059 0.01843814 0.01058649 2512/6288
## R-HSA-975634 2/10 43/10654 0.0007014985 0.01843814 0.01058649 4023/5950
## R-HSA-6806667 2/10 47/10654 0.0008380973 0.01843814 0.01058649 4023/5950
## R-HSA-381340 2/10 84/10654 0.0026530469 0.04011328 0.02303155 3952/4023
## R-HSA-2980736 2/10 90/10654 0.0030388851 0.04011328 0.02303155 1066/3952
## R-HSA-2187338 2/10 103/10654 0.0039599508 0.04355946 0.02501022 4023/5950
## Count
## R-HSA-2173782 2
## R-HSA-975634 2
## R-HSA-6806667 2
## R-HSA-381340 2
## R-HSA-2980736 2
## R-HSA-2187338 2
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## Warning in clusterProfiler::bitr(d$gene_name, fromType = "SYMBOL", toType =
## "ENTREZID", : 2.27% of input gene IDs are fail to map...## [1] "cluster_7"
## [1] "AEBP1" "APOD" "C1R" "C1S" "C3" "CCDC80"
## [7] "CFD" "CFH" "COL1A1" "COL1A2" "COL3A1" "COL6A1"
## [13] "COL6A2" "COL6A3" "CTSK" "CXCL12" "CXCL14" "DCN"
## [19] "FBLN1" "FBLN2" "GPC3" "GPNMB" "GPX3" "GSN"
## [25] "IFITM3" "IGFBP3" "IGFBP4" "IGFBP6" "LUM" "MFAP4"
## [31] "MGP" "MMP2" "MYOC" "PDGFRA" "PLPP3" "S100A4"
## [37] "S100A6" "SERPING1" "SLIT3" "SRPX" "SVEP1" "TIMP2"
## [43] "TNXB" "WISP2"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID
## GO:0005201 GO:0005201
## GO:0048407 GO:0048407
## GO:0001968 GO:0001968
## GO:0030020 GO:0030020
## GO:0019838 GO:0019838
## GO:0031994 GO:0031994
## GO:0061134 GO:0061134
## GO:0005178 GO:0005178
## GO:0005539 GO:0005539
## GO:0005518 GO:0005518
## GO:0005520 GO:0005520
## GO:0008201 GO:0008201
## GO:0030414 GO:0030414
## GO:0043394 GO:0043394
## GO:0002020 GO:0002020
## GO:1901681 GO:1901681
## GO:0004252 GO:0004252
## GO:0004866 GO:0004866
## GO:0008236 GO:0008236
## GO:0061135 GO:0061135
## GO:0017171 GO:0017171
## GO:0030021 GO:0030021
## GO:0004857 GO:0004857
## GO:0004175 GO:0004175
## GO:0001664 GO:0001664
## GO:0008009 GO:0008009
## GO:0050839 GO:0050839
## GO:0050840 GO:0050840
## GO:0048306 GO:0048306
## GO:0042379 GO:0042379
## GO:0017022 GO:0017022
## GO:0046332 GO:0046332
## Description
## GO:0005201 extracellular matrix structural constituent
## GO:0048407 platelet-derived growth factor binding
## GO:0001968 fibronectin binding
## GO:0030020 extracellular matrix structural constituent conferring tensile strength
## GO:0019838 growth factor binding
## GO:0031994 insulin-like growth factor I binding
## GO:0061134 peptidase regulator activity
## GO:0005178 integrin binding
## GO:0005539 glycosaminoglycan binding
## GO:0005518 collagen binding
## GO:0005520 insulin-like growth factor binding
## GO:0008201 heparin binding
## GO:0030414 peptidase inhibitor activity
## GO:0043394 proteoglycan binding
## GO:0002020 protease binding
## GO:1901681 sulfur compound binding
## GO:0004252 serine-type endopeptidase activity
## GO:0004866 endopeptidase inhibitor activity
## GO:0008236 serine-type peptidase activity
## GO:0061135 endopeptidase regulator activity
## GO:0017171 serine hydrolase activity
## GO:0030021 extracellular matrix structural constituent conferring compression resistance
## GO:0004857 enzyme inhibitor activity
## GO:0004175 endopeptidase activity
## GO:0001664 G protein-coupled receptor binding
## GO:0008009 chemokine activity
## GO:0050839 cell adhesion molecule binding
## GO:0050840 extracellular matrix binding
## GO:0048306 calcium-dependent protein binding
## GO:0042379 chemokine receptor binding
## GO:0017022 myosin binding
## GO:0046332 SMAD binding
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0005201 15/43 163/17697 1.832871e-20 1.649584e-18 8.296154e-19
## GO:0048407 5/43 11/17697 3.043317e-11 1.223042e-09 6.150973e-10
## GO:0001968 6/43 27/17697 4.076808e-11 1.223042e-09 6.150973e-10
## GO:0030020 6/43 41/17697 6.038994e-10 1.358774e-08 6.833598e-09
## GO:0019838 8/43 137/17697 1.211771e-09 2.181189e-08 1.096972e-08
## GO:0031994 3/43 12/17697 2.895130e-06 4.342695e-05 2.184045e-05
## GO:0061134 6/43 219/17697 1.394903e-05 1.793447e-04 9.019676e-05
## GO:0005178 5/43 132/17697 1.640569e-05 1.796401e-04 9.034534e-05
## GO:0005539 6/43 229/17697 1.796401e-05 1.796401e-04 9.034534e-05
## GO:0005518 4/43 67/17697 2.071866e-05 1.864679e-04 9.377919e-05
## GO:0005520 3/43 28/17697 4.195765e-05 3.432899e-04 1.726487e-04
## GO:0008201 5/43 169/17697 5.372092e-05 4.029069e-04 2.026316e-04
## GO:0030414 5/43 182/17697 7.635348e-05 5.286010e-04 2.658461e-04
## GO:0043394 3/43 36/17697 9.021554e-05 5.799570e-04 2.916743e-04
## GO:0002020 4/43 128/17697 2.590064e-04 1.554038e-03 7.815632e-04
## GO:1901681 5/43 250/17697 3.356712e-04 1.888150e-03 9.495961e-04
## GO:0004252 4/43 160/17697 6.034795e-04 3.194891e-03 1.606787e-03
## GO:0004866 4/43 175/17697 8.439349e-04 4.219675e-03 2.122176e-03
## GO:0008236 4/43 182/17697 9.765292e-04 4.394381e-03 2.210040e-03
## GO:0061135 4/43 182/17697 9.765292e-04 4.394381e-03 2.210040e-03
## GO:0017171 4/43 186/17697 1.058556e-03 4.536670e-03 2.281600e-03
## GO:0030021 2/43 22/17697 1.291656e-03 5.284048e-03 2.657474e-03
## GO:0004857 5/43 375/17697 2.066238e-03 8.085280e-03 4.066281e-03
## GO:0004175 5/43 427/17697 3.616286e-03 1.356107e-02 6.820188e-03
## GO:0001664 4/43 280/17697 4.661366e-03 1.678092e-02 8.439526e-03
## GO:0008009 2/43 49/17697 6.308217e-03 2.183614e-02 1.098192e-02
## GO:0050839 5/43 499/17697 6.955493e-03 2.318498e-02 1.166028e-02
## GO:0050840 2/43 57/17697 8.456738e-03 2.718237e-02 1.367067e-02
## GO:0048306 2/43 61/17697 9.637379e-03 2.990911e-02 1.504201e-02
## GO:0042379 2/43 66/17697 1.121011e-02 3.363033e-02 1.691350e-02
## GO:0017022 2/43 71/17697 1.288802e-02 3.741684e-02 1.881782e-02
## GO:0046332 2/43 80/17697 1.616489e-02 4.546376e-02 2.286481e-02
## geneID
## GO:0005201 165/1277/1278/1281/1291/1292/1293/1634/2192/2199/4060/4239/4256/8406/7148
## GO:0048407 1277/1278/1281/1291/5156
## GO:0001968 151887/1513/2192/3486/3489/4653
## GO:0030020 1277/1278/1281/1291/1292/1293
## GO:0019838 1277/1278/1281/1291/3486/3487/3489/5156
## GO:0031994 3486/3487/3489
## GO:0061134 718/1293/2192/2719/710/7077
## GO:0005178 1281/6387/10457/7077/7148
## GO:0005539 151887/3075/1634/10457/6586/7148
## GO:0005518 165/1513/1634/4060
## GO:0005520 3486/3487/3489
## GO:0008201 151887/3075/10457/6586/7148
## GO:0030414 718/1293/2719/710/7077
## GO:0043394 3075/1513/10457
## GO:0002020 1277/1278/1281/7077
## GO:1901681 151887/3075/10457/6586/7148
## GO:0004252 715/716/1675/4313
## GO:0004866 718/1293/710/7077
## GO:0008236 715/716/1675/4313
## GO:0061135 718/1293/710/7077
## GO:0017171 715/716/1675/4313
## GO:0030021 1634/4060
## GO:0004857 718/1293/2719/710/7077
## GO:0004175 715/716/1675/1513/4313
## GO:0001664 718/6387/9547/4653
## GO:0008009 6387/9547
## GO:0050839 1281/6387/10457/7077/7148
## GO:0050840 1634/2199
## GO:0048306 6275/6277
## GO:0042379 6387/9547
## GO:0017022 2934/4653
## GO:0046332 1278/1281
## Count
## GO:0005201 15
## GO:0048407 5
## GO:0001968 6
## GO:0030020 6
## GO:0019838 8
## GO:0031994 3
## GO:0061134 6
## GO:0005178 5
## GO:0005539 6
## GO:0005518 4
## GO:0005520 3
## GO:0008201 5
## GO:0030414 5
## GO:0043394 3
## GO:0002020 4
## GO:1901681 5
## GO:0004252 4
## GO:0004866 4
## GO:0008236 4
## GO:0061135 4
## GO:0017171 4
## GO:0030021 2
## GO:0004857 5
## GO:0004175 5
## GO:0001664 4
## GO:0008009 2
## GO:0050839 5
## GO:0050840 2
## GO:0048306 2
## GO:0042379 2
## GO:0017022 2
## GO:0046332 2
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-1474244 R-HSA-1474244
## R-HSA-3000178 R-HSA-3000178
## R-HSA-1474228 R-HSA-1474228
## R-HSA-1442490 R-HSA-1442490
## R-HSA-8948216 R-HSA-8948216
## R-HSA-216083 R-HSA-216083
## R-HSA-166658 R-HSA-166658
## R-HSA-2022090 R-HSA-2022090
## R-HSA-1650814 R-HSA-1650814
## R-HSA-977606 R-HSA-977606
## R-HSA-1474290 R-HSA-1474290
## R-HSA-166663 R-HSA-166663
## R-HSA-186797 R-HSA-186797
## R-HSA-381426 R-HSA-381426
## R-HSA-419037 R-HSA-419037
## R-HSA-3000480 R-HSA-3000480
## R-HSA-375165 R-HSA-375165
## R-HSA-3000170 R-HSA-3000170
## R-HSA-9006934 R-HSA-9006934
## R-HSA-8874081 R-HSA-8874081
## R-HSA-1592389 R-HSA-1592389
## R-HSA-2129379 R-HSA-2129379
## R-HSA-3560782 R-HSA-3560782
## R-HSA-8875878 R-HSA-8875878
## R-HSA-2173782 R-HSA-2173782
## R-HSA-8957275 R-HSA-8957275
## R-HSA-1566948 R-HSA-1566948
## R-HSA-430116 R-HSA-430116
## R-HSA-198933 R-HSA-198933
## R-HSA-3000171 R-HSA-3000171
## R-HSA-166786 R-HSA-166786
## R-HSA-2214320 R-HSA-2214320
## R-HSA-75892 R-HSA-75892
## R-HSA-2243919 R-HSA-2243919
## R-HSA-3560783 R-HSA-3560783
## R-HSA-3560801 R-HSA-3560801
## R-HSA-4420332 R-HSA-4420332
## R-HSA-6806834 R-HSA-6806834
## R-HSA-1971475 R-HSA-1971475
## R-HSA-114604 R-HSA-114604
## R-HSA-76009 R-HSA-76009
## R-HSA-1630316 R-HSA-1630316
## R-HSA-76002 R-HSA-76002
## R-HSA-3781865 R-HSA-3781865
## R-HSA-1793185 R-HSA-1793185
## R-HSA-1638091 R-HSA-1638091
## Description
## R-HSA-1474244 Extracellular matrix organization
## R-HSA-3000178 ECM proteoglycans
## R-HSA-1474228 Degradation of the extracellular matrix
## R-HSA-1442490 Collagen degradation
## R-HSA-8948216 Collagen chain trimerization
## R-HSA-216083 Integrin cell surface interactions
## R-HSA-166658 Complement cascade
## R-HSA-2022090 Assembly of collagen fibrils and other multimeric structures
## R-HSA-1650814 Collagen biosynthesis and modifying enzymes
## R-HSA-977606 Regulation of Complement cascade
## R-HSA-1474290 Collagen formation
## R-HSA-166663 Initial triggering of complement
## R-HSA-186797 Signaling by PDGF
## R-HSA-381426 Regulation of Insulin-like Growth Factor (IGF) transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs)
## R-HSA-419037 NCAM1 interactions
## R-HSA-3000480 Scavenging by Class A Receptors
## R-HSA-375165 NCAM signaling for neurite out-growth
## R-HSA-3000170 Syndecan interactions
## R-HSA-9006934 Signaling by Receptor Tyrosine Kinases
## R-HSA-8874081 MET activates PTK2 signaling
## R-HSA-1592389 Activation of Matrix Metalloproteinases
## R-HSA-2129379 Molecules associated with elastic fibres
## R-HSA-3560782 Diseases associated with glycosaminoglycan metabolism
## R-HSA-8875878 MET promotes cell motility
## R-HSA-2173782 Binding and Uptake of Ligands by Scavenger Receptors
## R-HSA-8957275 Post-translational protein phosphorylation
## R-HSA-1566948 Elastic fibre formation
## R-HSA-430116 GP1b-IX-V activation signalling
## R-HSA-198933 Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell
## R-HSA-3000171 Non-integrin membrane-ECM interactions
## R-HSA-166786 Creation of C4 and C2 activators
## R-HSA-2214320 Anchoring fibril formation
## R-HSA-75892 Platelet Adhesion to exposed collagen
## R-HSA-2243919 Crosslinking of collagen fibrils
## R-HSA-3560783 Defective B4GALT7 causes EDS, progeroid type
## R-HSA-3560801 Defective B3GAT3 causes JDSSDHD
## R-HSA-4420332 Defective B3GALT6 causes EDSP2 and SEMDJL1
## R-HSA-6806834 Signaling by MET
## R-HSA-1971475 A tetrasaccharide linker sequence is required for GAG synthesis
## R-HSA-114604 GPVI-mediated activation cascade
## R-HSA-76009 Platelet Aggregation (Plug Formation)
## R-HSA-1630316 Glycosaminoglycan metabolism
## R-HSA-76002 Platelet activation, signaling and aggregation
## R-HSA-3781865 Diseases of glycosylation
## R-HSA-1793185 Chondroitin sulfate/dermatan sulfate metabolism
## R-HSA-1638091 Heparan sulfate/heparin (HS-GAG) metabolism
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-1474244 15/34 301/10654 4.721437e-15 5.949011e-13 3.478954e-13
## R-HSA-3000178 9/34 76/10654 1.334713e-12 8.408689e-11 4.917362e-11
## R-HSA-1474228 10/34 140/10654 1.112901e-11 4.674184e-10 2.733441e-10
## R-HSA-1442490 8/34 64/10654 1.732511e-11 5.457410e-10 3.191468e-10
## R-HSA-8948216 6/34 44/10654 4.295783e-09 1.082537e-07 6.330628e-08
## R-HSA-216083 7/34 85/10654 7.234732e-09 1.519294e-07 8.884759e-08
## R-HSA-166658 6/34 58/10654 2.386414e-08 4.295546e-07 2.512015e-07
## R-HSA-2022090 6/34 61/10654 3.251631e-08 5.121319e-07 2.994924e-07
## R-HSA-1650814 6/34 67/10654 5.765409e-08 8.071573e-07 4.720218e-07
## R-HSA-977606 5/34 47/10654 3.394877e-07 3.911295e-06 2.287307e-06
## R-HSA-1474290 6/34 90/10654 3.414622e-07 3.911295e-06 2.287307e-06
## R-HSA-166663 4/34 23/10654 7.332862e-07 7.699506e-06 4.502635e-06
## R-HSA-186797 5/34 58/10654 9.890561e-07 9.586236e-06 5.605986e-06
## R-HSA-381426 6/34 125/10654 2.376501e-06 2.138851e-05 1.250790e-05
## R-HSA-419037 4/34 42/10654 8.880229e-06 7.459392e-05 4.362218e-05
## R-HSA-3000480 3/34 19/10654 2.778907e-05 2.188390e-04 1.279760e-04
## R-HSA-375165 4/34 63/10654 4.507352e-05 3.340743e-04 1.953651e-04
## R-HSA-3000170 3/34 27/10654 8.243173e-05 5.770221e-04 3.374398e-04
## R-HSA-9006934 8/34 473/10654 9.301072e-05 6.168079e-04 3.607064e-04
## R-HSA-8874081 3/34 30/10654 1.136717e-04 7.161320e-04 4.187906e-04
## R-HSA-1592389 3/34 33/10654 1.517608e-04 9.105645e-04 5.324939e-04
## R-HSA-2129379 3/34 38/10654 2.321066e-04 1.329338e-03 7.773904e-04
## R-HSA-3560782 3/34 41/10654 2.913858e-04 1.529775e-03 8.946055e-04
## R-HSA-8875878 3/34 41/10654 2.913858e-04 1.529775e-03 8.946055e-04
## R-HSA-2173782 3/34 42/10654 3.131170e-04 1.578110e-03 9.228711e-04
## R-HSA-8957275 4/34 108/10654 3.664270e-04 1.775762e-03 1.038457e-03
## R-HSA-1566948 3/34 45/10654 3.845106e-04 1.794383e-03 1.049347e-03
## R-HSA-430116 2/34 12/10654 6.395179e-04 2.877831e-03 1.682942e-03
## R-HSA-198933 4/34 132/10654 7.827131e-04 3.400753e-03 1.988745e-03
## R-HSA-3000171 3/34 59/10654 8.544569e-04 3.569612e-03 2.087492e-03
## R-HSA-166786 2/34 14/10654 8.782379e-04 3.569612e-03 2.087492e-03
## R-HSA-2214320 2/34 15/10654 1.011326e-03 3.861428e-03 2.258145e-03
## R-HSA-75892 2/34 15/10654 1.011326e-03 3.861428e-03 2.258145e-03
## R-HSA-2243919 2/34 18/10654 1.464833e-03 5.428499e-03 3.174561e-03
## R-HSA-3560783 2/34 20/10654 1.811816e-03 6.169970e-03 3.608169e-03
## R-HSA-3560801 2/34 20/10654 1.811816e-03 6.169970e-03 3.608169e-03
## R-HSA-4420332 2/34 20/10654 1.811816e-03 6.169970e-03 3.608169e-03
## R-HSA-6806834 3/34 79/10654 1.990029e-03 6.598517e-03 3.858782e-03
## R-HSA-1971475 2/34 26/10654 3.062242e-03 9.893398e-03 5.785613e-03
## R-HSA-114604 2/34 35/10654 5.506522e-03 1.734555e-02 1.014359e-02
## R-HSA-76009 2/34 39/10654 6.803276e-03 2.090763e-02 1.222668e-02
## R-HSA-1630316 3/34 124/10654 7.078589e-03 2.123577e-02 1.241858e-02
## R-HSA-76002 4/34 262/10654 9.281933e-03 2.719822e-02 1.590539e-02
## R-HSA-3781865 3/34 143/10654 1.045335e-02 2.993458e-02 1.750560e-02
## R-HSA-1793185 2/34 50/10654 1.100355e-02 3.080994e-02 1.801751e-02
## R-HSA-1638091 2/34 55/10654 1.320718e-02 3.617620e-02 2.115567e-02
## geneID
## R-HSA-1474244 1277/1278/1281/1291/1292/1293/1513/1634/2192/2199/4060/4239/4313/7077/7148
## R-HSA-3000178 1277/1278/1281/1291/1292/1293/1634/4060/7148
## R-HSA-1474228 1277/1278/1281/1291/1292/1293/1513/1634/4313/7077
## R-HSA-1442490 1277/1278/1281/1291/1292/1293/1513/4313
## R-HSA-8948216 1277/1278/1281/1291/1292/1293
## R-HSA-216083 1277/1278/1281/1291/1292/1293/4060
## R-HSA-166658 715/716/718/1675/3075/710
## R-HSA-2022090 1277/1278/1281/1291/1292/1293
## R-HSA-1650814 1277/1278/1281/1291/1292/1293
## R-HSA-977606 715/716/718/3075/710
## R-HSA-1474290 1277/1278/1281/1291/1292/1293
## R-HSA-166663 715/716/718/1675
## R-HSA-186797 1281/1291/1292/1293/5156
## R-HSA-381426 718/2719/3486/3487/3489/4313
## R-HSA-419037 1281/1291/1292/1293
## R-HSA-3000480 1277/1278/1281
## R-HSA-375165 1281/1291/1292/1293
## R-HSA-3000170 1277/1278/1281
## R-HSA-9006934 1277/1278/1281/1291/1292/1293/6387/5156
## R-HSA-8874081 1277/1278/1281
## R-HSA-1592389 1513/4313/7077
## R-HSA-2129379 2192/2199/4239
## R-HSA-3560782 1634/2719/4060
## R-HSA-8875878 1277/1278/1281
## R-HSA-2173782 1277/1278/1281
## R-HSA-8957275 718/2719/3486/3487
## R-HSA-1566948 2192/2199/4239
## R-HSA-430116 1277/1278
## R-HSA-198933 718/1277/1278/1281
## R-HSA-3000171 1277/1278/1281
## R-HSA-166786 715/716
## R-HSA-2214320 1277/1278
## R-HSA-75892 1277/1278
## R-HSA-2243919 1277/1278
## R-HSA-3560783 1634/2719
## R-HSA-3560801 1634/2719
## R-HSA-4420332 1634/2719
## R-HSA-6806834 1277/1278/1281
## R-HSA-1971475 1634/2719
## R-HSA-114604 1277/1278
## R-HSA-76009 1277/1278
## R-HSA-1630316 1634/2719/4060
## R-HSA-76002 1675/1277/1278/710
## R-HSA-3781865 1634/2719/4060
## R-HSA-1793185 1634/2719
## R-HSA-1638091 1634/2719
## Count
## R-HSA-1474244 15
## R-HSA-3000178 9
## R-HSA-1474228 10
## R-HSA-1442490 8
## R-HSA-8948216 6
## R-HSA-216083 7
## R-HSA-166658 6
## R-HSA-2022090 6
## R-HSA-1650814 6
## R-HSA-977606 5
## R-HSA-1474290 6
## R-HSA-166663 4
## R-HSA-186797 5
## R-HSA-381426 6
## R-HSA-419037 4
## R-HSA-3000480 3
## R-HSA-375165 4
## R-HSA-3000170 3
## R-HSA-9006934 8
## R-HSA-8874081 3
## R-HSA-1592389 3
## R-HSA-2129379 3
## R-HSA-3560782 3
## R-HSA-8875878 3
## R-HSA-2173782 3
## R-HSA-8957275 4
## R-HSA-1566948 3
## R-HSA-430116 2
## R-HSA-198933 4
## R-HSA-3000171 3
## R-HSA-166786 2
## R-HSA-2214320 2
## R-HSA-75892 2
## R-HSA-2243919 2
## R-HSA-3560783 2
## R-HSA-3560801 2
## R-HSA-4420332 2
## R-HSA-6806834 3
## R-HSA-1971475 2
## R-HSA-114604 2
## R-HSA-76009 2
## R-HSA-1630316 3
## R-HSA-76002 4
## R-HSA-3781865 3
## R-HSA-1793185 2
## R-HSA-1638091 2
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_8"
## [1] "A2M" "AIF1" "B2M" "C1QA" "C1QB" "C1QC" "CD14"
## [8] "CD163" "CD74" "CSF1R" "CST3" "CTSB" "CTSC" "CTSD"
## [15] "DAB2" "F13A1" "FAU" "FCGR2B" "FCGRT" "FOLR2" "FTL"
## [22] "GRN" "HLA-A" "HLA-B" "HLA-DRA" "HLA-E" "LGMN" "LYVE1"
## [29] "MAF" "MAFB" "MARCO" "MPEG1" "MRC1" "MS4A4A" "MS4A6A"
## [36] "NPC2" "PLTP" "PSAP" "RNASE1" "SELENOP" "SLC40A1" "STAB1"
## [43] "TMSB10" "TMSB4X" "TPT1" "TYROBP"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID Description GeneRatio
## GO:0033218 GO:0033218 amide binding 14/44
## GO:0042277 GO:0042277 peptide binding 11/44
## GO:0038024 GO:0038024 cargo receptor activity 6/44
## GO:0042605 GO:0042605 peptide antigen binding 4/44
## GO:0001540 GO:0001540 amyloid-beta binding 5/44
## GO:0005044 GO:0005044 scavenger receptor activity 3/44
## GO:0019864 GO:0019864 IgG binding 2/44
## GO:0097001 GO:0097001 ceramide binding 2/44
## GO:0003823 GO:0003823 antigen binding 4/44
## GO:0023026 GO:0023026 MHC class II protein complex binding 2/44
## GO:0008329 GO:0008329 signaling pattern recognition receptor activity 2/44
## GO:0005540 GO:0005540 hyaluronic acid binding 2/44
## GO:0038187 GO:0038187 pattern recognition receptor activity 2/44
## GO:0031406 GO:0031406 carboxylic acid binding 4/44
## GO:0046625 GO:0046625 sphingolipid binding 2/44
## GO:0019865 GO:0019865 immunoglobulin binding 2/44
## GO:0120013 GO:0120013 intermembrane lipid transfer activity 2/44
## GO:0043177 GO:0043177 organic acid binding 4/44
## GO:0023023 GO:0023023 MHC protein complex binding 2/44
## GO:0003785 GO:0003785 actin monomer binding 2/44
## GO:0004197 GO:0004197 cysteine-type endopeptidase activity 3/44
## GO:0002020 GO:0002020 protease binding 3/44
## GO:0019955 GO:0019955 cytokine binding 3/44
## GO:0042287 GO:0042287 MHC protein binding 2/44
## BgRatio pvalue p.adjust qvalue
## GO:0033218 356/17697 9.179048e-14 1.275888e-11 8.019589e-12
## GO:0042277 295/17697 1.078862e-10 7.498094e-09 4.712925e-09
## GO:0038024 85/17697 6.260799e-08 2.900837e-06 1.823320e-06
## GO:0042605 31/17697 9.956612e-07 3.459923e-05 2.174734e-05
## GO:0001540 78/17697 1.386723e-06 3.855090e-05 2.423116e-05
## GO:0005044 51/17697 2.747463e-04 6.364956e-03 4.000692e-03
## GO:0019864 11/17697 3.275843e-04 6.504889e-03 4.088646e-03
## GO:0097001 14/17697 5.394383e-04 9.372740e-03 5.891234e-03
## GO:0003823 160/17697 6.591923e-04 9.856520e-03 6.195313e-03
## GO:0023026 16/17697 7.091021e-04 9.856520e-03 6.195313e-03
## GO:0008329 20/17697 1.115673e-03 1.275668e-02 8.018208e-03
## GO:0005540 21/17697 1.231166e-03 1.275668e-02 8.018208e-03
## GO:0038187 21/17697 1.231166e-03 1.275668e-02 8.018208e-03
## GO:0031406 193/17697 1.323682e-03 1.275668e-02 8.018208e-03
## GO:0046625 23/17697 1.478586e-03 1.275668e-02 8.018208e-03
## GO:0019865 24/17697 1.610459e-03 1.275668e-02 8.018208e-03
## GO:0120013 24/17697 1.610459e-03 1.275668e-02 8.018208e-03
## GO:0043177 205/17697 1.651944e-03 1.275668e-02 8.018208e-03
## GO:0023023 25/17697 1.747738e-03 1.278609e-02 8.036692e-03
## GO:0003785 28/17697 2.191752e-03 1.523268e-02 9.574497e-03
## GO:0004197 116/17697 2.988440e-03 1.978063e-02 1.243311e-02
## GO:0002020 128/17697 3.942609e-03 2.382707e-02 1.497650e-02
## GO:0019955 128/17697 3.942609e-03 2.382707e-02 1.497650e-02
## GO:0042287 40/17697 4.437947e-03 2.570311e-02 1.615568e-02
## geneID
## GO:0033218 712/929/972/1471/2213/2350/3105/3106/3122/3133/8685/5360/5660/30061
## GO:0042277 712/929/972/1471/2213/3105/3106/3122/3133/8685/30061
## GO:0038024 9332/1601/2350/8685/4360/23166
## GO:0042605 3105/3106/3122/3133
## GO:0001540 712/972/1471/2213/8685
## GO:0005044 9332/8685/23166
## GO:0019864 2213/2217
## GO:0097001 5360/5660
## GO:0003823 3105/3106/3122/3133
## GO:0023026 972/3122
## GO:0008329 929/8685
## GO:0005540 10894/23166
## GO:0038187 929/8685
## GO:0031406 2350/10894/5660/23166
## GO:0046625 5360/5660
## GO:0019865 2213/2217
## GO:0120013 10577/5360
## GO:0043177 2350/10894/5660/23166
## GO:0023023 972/3122
## GO:0003785 9168/7114
## GO:0004197 1508/1075/5641
## GO:0002020 2/1471/5660
## GO:0019955 2/972/1436
## GO:0042287 972/3133
## Count
## GO:0033218 14
## GO:0042277 11
## GO:0038024 6
## GO:0042605 4
## GO:0001540 5
## GO:0005044 3
## GO:0019864 2
## GO:0097001 2
## GO:0003823 4
## GO:0023026 2
## GO:0008329 2
## GO:0005540 2
## GO:0038187 2
## GO:0031406 4
## GO:0046625 2
## GO:0019865 2
## GO:0120013 2
## GO:0043177 4
## GO:0023023 2
## GO:0003785 2
## GO:0004197 3
## GO:0002020 3
## GO:0019955 3
## GO:0042287 2
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-1236977 R-HSA-1236977
## R-HSA-6798695 R-HSA-6798695
## R-HSA-1236975 R-HSA-1236975
## R-HSA-983170 R-HSA-983170
## R-HSA-2132295 R-HSA-2132295
## R-HSA-198933 R-HSA-198933
## R-HSA-1236974 R-HSA-1236974
## R-HSA-2173782 R-HSA-2173782
## R-HSA-166786 R-HSA-166786
## R-HSA-877300 R-HSA-877300
## R-HSA-166663 R-HSA-166663
## R-HSA-114608 R-HSA-114608
## R-HSA-76005 R-HSA-76005
## R-HSA-2424491 R-HSA-2424491
## R-HSA-2172127 R-HSA-2172127
## R-HSA-977606 R-HSA-977606
## R-HSA-913531 R-HSA-913531
## R-HSA-1679131 R-HSA-1679131
## R-HSA-166658 R-HSA-166658
## R-HSA-983169 R-HSA-983169
## R-HSA-174824 R-HSA-174824
## R-HSA-909733 R-HSA-909733
## R-HSA-3000480 R-HSA-3000480
## R-HSA-76002 R-HSA-76002
## R-HSA-140877 R-HSA-140877
## Description
## R-HSA-1236977 Endosomal/Vacuolar pathway
## R-HSA-6798695 Neutrophil degranulation
## R-HSA-1236975 Antigen processing-Cross presentation
## R-HSA-983170 Antigen Presentation: Folding, assembly and peptide loading of class I MHC
## R-HSA-2132295 MHC class II antigen presentation
## R-HSA-198933 Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell
## R-HSA-1236974 ER-Phagosome pathway
## R-HSA-2173782 Binding and Uptake of Ligands by Scavenger Receptors
## R-HSA-166786 Creation of C4 and C2 activators
## R-HSA-877300 Interferon gamma signaling
## R-HSA-166663 Initial triggering of complement
## R-HSA-114608 Platelet degranulation
## R-HSA-76005 Response to elevated platelet cytosolic Ca2+
## R-HSA-2424491 DAP12 signaling
## R-HSA-2172127 DAP12 interactions
## R-HSA-977606 Regulation of Complement cascade
## R-HSA-913531 Interferon Signaling
## R-HSA-1679131 Trafficking and processing of endosomal TLR
## R-HSA-166658 Complement cascade
## R-HSA-983169 Class I MHC mediated antigen processing & presentation
## R-HSA-174824 Plasma lipoprotein assembly, remodeling, and clearance
## R-HSA-909733 Interferon alpha/beta signaling
## R-HSA-3000480 Scavenging by Class A Receptors
## R-HSA-76002 Platelet activation, signaling and aggregation
## R-HSA-140877 Formation of Fibrin Clot (Clotting Cascade)
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-1236977 4/38 11/10654 4.459540e-08 4.019555e-06 3.098841e-06
## R-HSA-6798695 12/38 480/10654 5.661345e-08 4.019555e-06 3.098841e-06
## R-HSA-1236975 6/38 99/10654 1.199505e-06 5.677655e-05 4.377140e-05
## R-HSA-983170 4/38 25/10654 1.649394e-06 5.855348e-05 4.514130e-05
## R-HSA-2132295 6/38 123/10654 4.275123e-06 1.214135e-04 9.360270e-05
## R-HSA-198933 6/38 132/10654 6.435270e-06 1.523014e-04 1.174155e-04
## R-HSA-1236974 5/38 83/10654 1.042276e-05 2.114332e-04 1.630026e-04
## R-HSA-2173782 4/38 42/10654 1.397386e-05 2.340110e-04 1.804088e-04
## R-HSA-166786 3/38 14/10654 1.483168e-05 2.340110e-04 1.804088e-04
## R-HSA-877300 5/38 92/10654 1.724738e-05 2.449128e-04 1.888134e-04
## R-HSA-166663 3/38 23/10654 7.057904e-05 9.111113e-04 7.024134e-04
## R-HSA-114608 5/38 129/10654 8.771981e-05 1.038018e-03 8.002509e-04
## R-HSA-76005 5/38 134/10654 1.050369e-04 1.147326e-03 8.845212e-04
## R-HSA-2424491 3/38 30/10654 1.590367e-04 1.613086e-03 1.243595e-03
## R-HSA-2172127 3/38 43/10654 4.682010e-04 4.432303e-03 3.417046e-03
## R-HSA-977606 3/38 47/10654 6.091563e-04 5.406262e-03 4.167912e-03
## R-HSA-913531 5/38 199/10654 6.574446e-04 5.491596e-03 4.233699e-03
## R-HSA-1679131 2/38 13/10654 9.426078e-04 7.436128e-03 5.732819e-03
## R-HSA-166658 3/38 58/10654 1.128280e-03 8.432409e-03 6.500894e-03
## R-HSA-983169 6/38 371/10654 1.838797e-03 1.203657e-02 9.279489e-03
## R-HSA-174824 3/38 69/10654 1.864820e-03 1.203657e-02 9.279489e-03
## R-HSA-909733 3/38 69/10654 1.864820e-03 1.203657e-02 9.279489e-03
## R-HSA-3000480 2/38 19/10654 2.038787e-03 1.258729e-02 9.704065e-03
## R-HSA-76002 5/38 262/10654 2.237056e-03 1.323592e-02 1.020412e-02
## R-HSA-140877 2/38 39/10654 8.447210e-03 4.798015e-02 3.698989e-02
## geneID Count
## R-HSA-1236977 567/3105/3106/3133 4
## R-HSA-6798695 567/929/1471/1508/1075/1509/2512/2896/3106/10577/5660/7305 12
## R-HSA-1236975 567/929/3105/3106/3133/4360 6
## R-HSA-983170 567/3105/3106/3133 4
## R-HSA-2132295 972/1508/1075/1509/3122/5641 6
## R-HSA-198933 567/2213/3105/3106/3133/7305 6
## R-HSA-1236974 567/929/3105/3106/3133 5
## R-HSA-2173782 9332/2512/8685/23166 4
## R-HSA-166786 712/713/714 3
## R-HSA-877300 567/3105/3106/3122/3133 5
## R-HSA-166663 712/713/714 3
## R-HSA-114608 2/2162/5660/6414/7114 5
## R-HSA-76005 2/2162/5660/6414/7114 5
## R-HSA-2424491 567/3133/7305 3
## R-HSA-2172127 567/3133/7305 3
## R-HSA-977606 712/713/714 3
## R-HSA-913531 567/3105/3106/3122/3133 5
## R-HSA-1679131 1508/5641 2
## R-HSA-166658 712/713/714 3
## R-HSA-983169 567/929/3105/3106/3133/4360 6
## R-HSA-174824 2/10577/5360 3
## R-HSA-909733 3105/3106/3133 3
## R-HSA-3000480 2512/8685 2
## R-HSA-76002 2/2162/5660/6414/7114 5
## R-HSA-140877 2/2162 2
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_9"
## [1] "ABI3BP" "ACKR3" "ACTG1" "ADD3" "AHNAK" "C1R" "C1S"
## [8] "C3" "CCDC80" "CD55" "CFD" "CILP" "CLEC3B" "COL1A1"
## [15] "COL1A2" "COL3A1" "COL6A1" "COL6A2" "COL6A3" "CPE" "DCN"
## [22] "EFEMP1" "EMP3" "FBLN1" "FBLN2" "FBN1" "FN1" "FNDC1"
## [29] "FSTL1" "GPNMB" "GSN" "HTRA3" "IGFBP5" "IGFBP6" "LUM"
## [36] "MARCKS" "MFAP5" "MMP2" "MYADM" "PI16" "PMP22" "PRG4"
## [43] "PTGIS" "S100A10" "S100A4" "S100A6" "SCARA5" "SEMA3C" "SFRP4"
## [50] "TIMP2" "TNXB" "VCAN"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID
## GO:0005201 GO:0005201
## GO:0005539 GO:0005539
## GO:0030020 GO:0030020
## GO:0048407 GO:0048407
## GO:0008201 GO:0008201
## GO:0019838 GO:0019838
## GO:0030021 GO:0030021
## GO:1901681 GO:1901681
## GO:0001968 GO:0001968
## GO:0005178 GO:0005178
## GO:0097493 GO:0097493
## GO:0002020 GO:0002020
## GO:0061134 GO:0061134
## GO:0005518 GO:0005518
## GO:0005520 GO:0005520
## GO:0050839 GO:0050839
## GO:0004252 GO:0004252
## GO:0008236 GO:0008236
## GO:0017171 GO:0017171
## GO:0005044 GO:0005044
## GO:0031994 GO:0031994
## GO:0044548 GO:0044548
## GO:0030414 GO:0030414
## GO:0038024 GO:0038024
## GO:0043394 GO:0043394
## GO:0016504 GO:0016504
## GO:0004175 GO:0004175
## GO:0050840 GO:0050840
## GO:0048306 GO:0048306
## GO:0004866 GO:0004866
## Description
## GO:0005201 extracellular matrix structural constituent
## GO:0005539 glycosaminoglycan binding
## GO:0030020 extracellular matrix structural constituent conferring tensile strength
## GO:0048407 platelet-derived growth factor binding
## GO:0008201 heparin binding
## GO:0019838 growth factor binding
## GO:0030021 extracellular matrix structural constituent conferring compression resistance
## GO:1901681 sulfur compound binding
## GO:0001968 fibronectin binding
## GO:0005178 integrin binding
## GO:0097493 structural molecule activity conferring elasticity
## GO:0002020 protease binding
## GO:0061134 peptidase regulator activity
## GO:0005518 collagen binding
## GO:0005520 insulin-like growth factor binding
## GO:0050839 cell adhesion molecule binding
## GO:0004252 serine-type endopeptidase activity
## GO:0008236 serine-type peptidase activity
## GO:0017171 serine hydrolase activity
## GO:0005044 scavenger receptor activity
## GO:0031994 insulin-like growth factor I binding
## GO:0044548 S100 protein binding
## GO:0030414 peptidase inhibitor activity
## GO:0038024 cargo receptor activity
## GO:0043394 proteoglycan binding
## GO:0016504 peptidase activator activity
## GO:0004175 endopeptidase activity
## GO:0050840 extracellular matrix binding
## GO:0048306 calcium-dependent protein binding
## GO:0004866 endopeptidase inhibitor activity
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0005201 19/51 163/17697 2.686161e-26 2.793607e-24 1.639972e-24
## GO:0005539 10/51 229/17697 8.689308e-10 4.518440e-08 2.652525e-08
## GO:0030020 6/51 41/17697 1.759900e-09 6.100986e-08 3.581551e-08
## GO:0048407 4/51 11/17697 1.988719e-08 5.170670e-07 3.035414e-07
## GO:0008201 8/51 169/17697 2.630203e-08 5.470822e-07 3.211616e-07
## GO:0019838 7/51 137/17697 1.244806e-07 2.157665e-06 1.266645e-06
## GO:0030021 4/51 22/17697 4.306415e-07 6.398103e-06 3.755971e-06
## GO:1901681 8/51 250/17697 5.344217e-07 6.947482e-06 4.078481e-06
## GO:0001968 4/51 27/17697 1.022264e-06 1.181283e-05 6.934656e-06
## GO:0005178 6/51 132/17697 2.100892e-06 2.184928e-05 1.282650e-05
## GO:0097493 3/51 11/17697 3.660337e-06 3.460682e-05 2.031574e-05
## GO:0002020 5/51 128/17697 3.293044e-05 2.853971e-04 1.675408e-04
## GO:0061134 6/51 219/17697 3.794776e-05 3.035821e-04 1.782162e-04
## GO:0005518 4/51 67/17697 4.101074e-05 3.046512e-04 1.788438e-04
## GO:0005520 3/51 28/17697 7.020473e-05 4.867528e-04 2.857455e-04
## GO:0050839 8/51 499/17697 8.280610e-05 5.382396e-04 3.159706e-04
## GO:0004252 5/51 160/17697 9.528047e-05 5.828923e-04 3.421837e-04
## GO:0008236 5/51 182/17697 1.743551e-04 1.007385e-03 5.913798e-04
## GO:0017171 5/51 186/17697 1.929335e-04 1.056057e-03 6.199524e-04
## GO:0005044 3/51 51/17697 4.259233e-04 2.214801e-03 1.300187e-03
## GO:0031994 2/51 12/17697 5.275836e-04 2.612795e-03 1.533827e-03
## GO:0044548 2/51 15/17697 8.347073e-04 3.945889e-03 2.316413e-03
## GO:0030414 4/51 182/17697 1.855199e-03 8.171503e-03 4.797036e-03
## GO:0038024 3/51 85/17697 1.885732e-03 8.171503e-03 4.797036e-03
## GO:0043394 2/51 36/17697 4.818446e-03 2.004474e-02 1.176715e-02
## GO:0016504 2/51 38/17697 5.357071e-03 2.142828e-02 1.257936e-02
## GO:0004175 5/51 427/17697 7.545067e-03 2.906248e-02 1.706097e-02
## GO:0050840 2/51 57/17697 1.174598e-02 4.362793e-02 2.561154e-02
## GO:0048306 2/51 61/17697 1.337003e-02 4.794770e-02 2.814744e-02
## GO:0004866 3/51 175/17697 1.398635e-02 4.848603e-02 2.846346e-02
## geneID
## GO:0005201 25890/8483/1277/1278/1281/1291/1292/1293/1634/2202/2192/2199/2200/2335/4060/8076/10216/7148/1462
## GO:0005539 25890/151887/7123/1634/2200/2335/11167/10457/7148/1462
## GO:0030020 1277/1278/1281/1291/1292/1293
## GO:0048407 1277/1278/1281/1291
## GO:0008201 25890/151887/7123/2200/2335/11167/10457/7148
## GO:0019838 1277/1278/1281/1291/94031/3488/3489
## GO:0030021 1634/4060/10216/1462
## GO:1901681 25890/151887/7123/2200/2335/11167/10457/7148
## GO:0001968 151887/2192/3488/3489
## GO:0005178 1281/2200/2335/10457/7077/7148
## GO:0097493 79026/2199/2200
## GO:0002020 1277/1278/1281/2335/7077
## GO:0061134 718/1293/2192/2335/221476/7077
## GO:0005518 25890/1634/2335/4060
## GO:0005520 94031/3488/3489
## GO:0050839 79026/1281/1363/2200/2335/10457/7077/7148
## GO:0004252 715/716/1675/94031/4313
## GO:0008236 715/716/1675/94031/4313
## GO:0017171 715/716/1675/94031/4313
## GO:0005044 57007/10216/286133
## GO:0031994 3488/3489
## GO:0044548 79026/6277
## GO:0030414 718/1293/221476/7077
## GO:0038024 57007/10216/286133
## GO:0043394 2335/10457
## GO:0016504 2192/2335
## GO:0004175 715/716/1675/94031/4313
## GO:0050840 1634/2199
## GO:0048306 6275/6277
## GO:0004866 718/1293/7077
## Count
## GO:0005201 19
## GO:0005539 10
## GO:0030020 6
## GO:0048407 4
## GO:0008201 8
## GO:0019838 7
## GO:0030021 4
## GO:1901681 8
## GO:0001968 4
## GO:0005178 6
## GO:0097493 3
## GO:0002020 5
## GO:0061134 6
## GO:0005518 4
## GO:0005520 3
## GO:0050839 8
## GO:0004252 5
## GO:0008236 5
## GO:0017171 5
## GO:0005044 3
## GO:0031994 2
## GO:0044548 2
## GO:0030414 4
## GO:0038024 3
## GO:0043394 2
## GO:0016504 2
## GO:0004175 5
## GO:0050840 2
## GO:0048306 2
## GO:0004866 3
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-1474244 R-HSA-1474244
## R-HSA-3000178 R-HSA-3000178
## R-HSA-1474228 R-HSA-1474228
## R-HSA-216083 R-HSA-216083
## R-HSA-1442490 R-HSA-1442490
## R-HSA-2129379 R-HSA-2129379
## R-HSA-8948216 R-HSA-8948216
## R-HSA-1566948 R-HSA-1566948
## R-HSA-381426 R-HSA-381426
## R-HSA-2022090 R-HSA-2022090
## R-HSA-1650814 R-HSA-1650814
## R-HSA-3000480 R-HSA-3000480
## R-HSA-1474290 R-HSA-1474290
## R-HSA-166663 R-HSA-166663
## R-HSA-166658 R-HSA-166658
## R-HSA-8957275 R-HSA-8957275
## R-HSA-3000170 R-HSA-3000170
## R-HSA-8874081 R-HSA-8874081
## R-HSA-8875878 R-HSA-8875878
## R-HSA-2173782 R-HSA-2173782
## R-HSA-419037 R-HSA-419037
## R-HSA-977606 R-HSA-977606
## R-HSA-9006934 R-HSA-9006934
## R-HSA-186797 R-HSA-186797
## R-HSA-3000171 R-HSA-3000171
## R-HSA-375165 R-HSA-375165
## R-HSA-6806834 R-HSA-6806834
## R-HSA-76009 R-HSA-76009
## R-HSA-3560782 R-HSA-3560782
## R-HSA-2022923 R-HSA-2022923
## R-HSA-430116 R-HSA-430116
## R-HSA-166786 R-HSA-166786
## R-HSA-2024101 R-HSA-2024101
## R-HSA-198933 R-HSA-198933
## R-HSA-2214320 R-HSA-2214320
## R-HSA-75892 R-HSA-75892
## R-HSA-2243919 R-HSA-2243919
## R-HSA-76002 R-HSA-76002
## R-HSA-2022870 R-HSA-2022870
## R-HSA-3560783 R-HSA-3560783
## R-HSA-3560801 R-HSA-3560801
## R-HSA-4420332 R-HSA-4420332
## R-HSA-1971475 R-HSA-1971475
## R-HSA-1592389 R-HSA-1592389
## R-HSA-6785807 R-HSA-6785807
## R-HSA-114604 R-HSA-114604
## R-HSA-6802948 R-HSA-6802948
## R-HSA-5674135 R-HSA-5674135
## R-HSA-1630316 R-HSA-1630316
## R-HSA-114608 R-HSA-114608
## R-HSA-76005 R-HSA-76005
## R-HSA-6802946 R-HSA-6802946
## R-HSA-6802949 R-HSA-6802949
## R-HSA-6802955 R-HSA-6802955
## R-HSA-9649948 R-HSA-9649948
## R-HSA-202733 R-HSA-202733
## R-HSA-1793185 R-HSA-1793185
## R-HSA-3928665 R-HSA-3928665
## R-HSA-3781865 R-HSA-3781865
## R-HSA-1638091 R-HSA-1638091
## Description
## R-HSA-1474244 Extracellular matrix organization
## R-HSA-3000178 ECM proteoglycans
## R-HSA-1474228 Degradation of the extracellular matrix
## R-HSA-216083 Integrin cell surface interactions
## R-HSA-1442490 Collagen degradation
## R-HSA-2129379 Molecules associated with elastic fibres
## R-HSA-8948216 Collagen chain trimerization
## R-HSA-1566948 Elastic fibre formation
## R-HSA-381426 Regulation of Insulin-like Growth Factor (IGF) transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs)
## R-HSA-2022090 Assembly of collagen fibrils and other multimeric structures
## R-HSA-1650814 Collagen biosynthesis and modifying enzymes
## R-HSA-3000480 Scavenging by Class A Receptors
## R-HSA-1474290 Collagen formation
## R-HSA-166663 Initial triggering of complement
## R-HSA-166658 Complement cascade
## R-HSA-8957275 Post-translational protein phosphorylation
## R-HSA-3000170 Syndecan interactions
## R-HSA-8874081 MET activates PTK2 signaling
## R-HSA-8875878 MET promotes cell motility
## R-HSA-2173782 Binding and Uptake of Ligands by Scavenger Receptors
## R-HSA-419037 NCAM1 interactions
## R-HSA-977606 Regulation of Complement cascade
## R-HSA-9006934 Signaling by Receptor Tyrosine Kinases
## R-HSA-186797 Signaling by PDGF
## R-HSA-3000171 Non-integrin membrane-ECM interactions
## R-HSA-375165 NCAM signaling for neurite out-growth
## R-HSA-6806834 Signaling by MET
## R-HSA-76009 Platelet Aggregation (Plug Formation)
## R-HSA-3560782 Diseases associated with glycosaminoglycan metabolism
## R-HSA-2022923 Dermatan sulfate biosynthesis
## R-HSA-430116 GP1b-IX-V activation signalling
## R-HSA-166786 Creation of C4 and C2 activators
## R-HSA-2024101 CS/DS degradation
## R-HSA-198933 Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell
## R-HSA-2214320 Anchoring fibril formation
## R-HSA-75892 Platelet Adhesion to exposed collagen
## R-HSA-2243919 Crosslinking of collagen fibrils
## R-HSA-76002 Platelet activation, signaling and aggregation
## R-HSA-2022870 Chondroitin sulfate biosynthesis
## R-HSA-3560783 Defective B4GALT7 causes EDS, progeroid type
## R-HSA-3560801 Defective B3GAT3 causes JDSSDHD
## R-HSA-4420332 Defective B3GALT6 causes EDSP2 and SEMDJL1
## R-HSA-1971475 A tetrasaccharide linker sequence is required for GAG synthesis
## R-HSA-1592389 Activation of Matrix Metalloproteinases
## R-HSA-6785807 Interleukin-4 and Interleukin-13 signaling
## R-HSA-114604 GPVI-mediated activation cascade
## R-HSA-6802948 Signaling by high-kinase activity BRAF mutants
## R-HSA-5674135 MAP2K and MAPK activation
## R-HSA-1630316 Glycosaminoglycan metabolism
## R-HSA-114608 Platelet degranulation
## R-HSA-76005 Response to elevated platelet cytosolic Ca2+
## R-HSA-6802946 Signaling by moderate kinase activity BRAF mutants
## R-HSA-6802949 Signaling by RAS mutants
## R-HSA-6802955 Paradoxical activation of RAF signaling by kinase inactive BRAF
## R-HSA-9649948 Signaling downstream of RAS mutants
## R-HSA-202733 Cell surface interactions at the vascular wall
## R-HSA-1793185 Chondroitin sulfate/dermatan sulfate metabolism
## R-HSA-3928665 EPH-ephrin mediated repulsion of cells
## R-HSA-3781865 Diseases of glycosylation
## R-HSA-1638091 Heparan sulfate/heparin (HS-GAG) metabolism
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-1474244 18/38 301/10654 1.600710e-18 2.465093e-16 1.381665e-16
## R-HSA-3000178 11/38 76/10654 1.183612e-15 9.113810e-14 5.108219e-14
## R-HSA-1474228 11/38 140/10654 1.205930e-12 6.190440e-11 3.469693e-11
## R-HSA-216083 9/38 85/10654 1.146146e-11 4.412661e-10 2.473262e-10
## R-HSA-1442490 7/38 64/10654 2.196778e-09 6.766076e-08 3.792333e-08
## R-HSA-2129379 6/38 38/10654 3.459710e-09 8.879923e-08 4.977127e-08
## R-HSA-8948216 6/38 44/10654 8.710488e-09 1.785762e-07 1.000905e-07
## R-HSA-1566948 6/38 45/10654 1.002465e-08 1.785762e-07 1.000905e-07
## R-HSA-381426 8/38 125/10654 1.043627e-08 1.785762e-07 1.000905e-07
## R-HSA-2022090 6/38 61/10654 6.557235e-08 1.009814e-06 5.659929e-07
## R-HSA-1650814 6/38 67/10654 1.160405e-07 1.624567e-06 9.105570e-07
## R-HSA-3000480 4/38 19/10654 5.131895e-07 6.585932e-06 3.691363e-06
## R-HSA-1474290 6/38 90/10654 6.821867e-07 8.081288e-06 4.529498e-06
## R-HSA-166663 4/38 23/10654 1.160492e-06 1.276542e-05 7.154916e-06
## R-HSA-166658 5/38 58/10654 1.754765e-06 1.801559e-05 1.009759e-05
## R-HSA-8957275 6/38 108/10654 2.001299e-06 1.926251e-05 1.079648e-05
## R-HSA-3000170 4/38 27/10654 2.276623e-06 2.062353e-05 1.155932e-05
## R-HSA-8874081 4/38 30/10654 3.527885e-06 3.018301e-05 1.691734e-05
## R-HSA-8875878 4/38 41/10654 1.267535e-05 1.024750e-04 5.743642e-05
## R-HSA-2173782 4/38 42/10654 1.397386e-05 1.024750e-04 5.743642e-05
## R-HSA-419037 4/38 42/10654 1.397386e-05 1.024750e-04 5.743642e-05
## R-HSA-977606 4/38 47/10654 2.198534e-05 1.538974e-04 8.625828e-05
## R-HSA-9006934 9/38 473/10654 3.199443e-05 2.142236e-04 1.200706e-04
## R-HSA-186797 4/38 58/10654 5.084723e-05 3.262697e-04 1.828716e-04
## R-HSA-3000171 4/38 59/10654 5.440613e-05 3.351418e-04 1.878443e-04
## R-HSA-375165 4/38 63/10654 7.048288e-05 4.174755e-04 2.339918e-04
## R-HSA-6806834 4/38 79/10654 1.706727e-04 9.734662e-04 5.456202e-04
## R-HSA-76009 3/38 39/10654 3.501487e-04 1.925818e-03 1.079406e-03
## R-HSA-3560782 3/38 41/10654 4.064198e-04 2.158229e-03 1.209671e-03
## R-HSA-2022923 2/38 11/10654 6.676585e-04 3.427314e-03 1.920982e-03
## R-HSA-430116 2/38 12/10654 7.993885e-04 3.971156e-03 2.225802e-03
## R-HSA-166786 2/38 14/10654 1.097237e-03 5.120440e-03 2.869966e-03
## R-HSA-2024101 2/38 14/10654 1.097237e-03 5.120440e-03 2.869966e-03
## R-HSA-198933 4/38 132/10654 1.199090e-03 5.403679e-03 3.028720e-03
## R-HSA-2214320 2/38 15/10654 1.263198e-03 5.403679e-03 3.028720e-03
## R-HSA-75892 2/38 15/10654 1.263198e-03 5.403679e-03 3.028720e-03
## R-HSA-2243919 2/38 18/10654 1.828283e-03 7.609610e-03 4.265127e-03
## R-HSA-76002 5/38 262/10654 2.237056e-03 8.287522e-03 4.645091e-03
## R-HSA-2022870 2/38 20/10654 2.260233e-03 8.287522e-03 4.645091e-03
## R-HSA-3560783 2/38 20/10654 2.260233e-03 8.287522e-03 4.645091e-03
## R-HSA-3560801 2/38 20/10654 2.260233e-03 8.287522e-03 4.645091e-03
## R-HSA-4420332 2/38 20/10654 2.260233e-03 8.287522e-03 4.645091e-03
## R-HSA-1971475 2/38 26/10654 3.814443e-03 1.366103e-02 7.656900e-03
## R-HSA-1592389 2/38 33/10654 6.100483e-03 2.135169e-02 1.196746e-02
## R-HSA-6785807 3/38 108/10654 6.604855e-03 2.260328e-02 1.266896e-02
## R-HSA-114604 2/38 35/10654 6.843864e-03 2.291207e-02 1.284203e-02
## R-HSA-6802948 2/38 36/10654 7.230233e-03 2.369055e-02 1.327837e-02
## R-HSA-5674135 2/38 40/10654 8.871926e-03 2.846410e-02 1.595390e-02
## R-HSA-1630316 3/38 124/10654 9.648915e-03 3.032516e-02 1.699702e-02
## R-HSA-114608 3/38 129/10654 1.074241e-02 3.308664e-02 1.854480e-02
## R-HSA-76005 3/38 134/10654 1.190525e-02 3.389305e-02 1.899679e-02
## R-HSA-6802946 2/38 47/10654 1.210466e-02 3.389305e-02 1.899679e-02
## R-HSA-6802949 2/38 47/10654 1.210466e-02 3.389305e-02 1.899679e-02
## R-HSA-6802955 2/38 47/10654 1.210466e-02 3.389305e-02 1.899679e-02
## R-HSA-9649948 2/38 47/10654 1.210466e-02 3.389305e-02 1.899679e-02
## R-HSA-202733 3/38 137/10654 1.263654e-02 3.475050e-02 1.947738e-02
## R-HSA-1793185 2/38 50/10654 1.362553e-02 3.681284e-02 2.063331e-02
## R-HSA-3928665 2/38 51/10654 1.415006e-02 3.700025e-02 2.073835e-02
## R-HSA-3781865 3/38 143/10654 1.417542e-02 3.700025e-02 2.073835e-02
## R-HSA-1638091 2/38 55/10654 1.633424e-02 4.192456e-02 2.349838e-02
## geneID
## R-HSA-1474244 1277/1278/1281/1291/1292/1293/1634/2202/2192/2199/2200/2335/4060/8076/4313/7077/7148/1462
## R-HSA-3000178 1277/1278/1281/1291/1292/1293/1634/2335/4060/7148/1462
## R-HSA-1474228 1277/1278/1281/1291/1292/1293/1634/2200/2335/4313/7077
## R-HSA-216083 1277/1278/1281/1291/1292/1293/2200/2335/4060
## R-HSA-1442490 1277/1278/1281/1291/1292/1293/4313
## R-HSA-2129379 2202/2192/2199/2200/2335/8076
## R-HSA-8948216 1277/1278/1281/1291/1292/1293
## R-HSA-1566948 2202/2192/2199/2200/2335/8076
## R-HSA-381426 718/2200/2335/11167/3488/3489/4313/1462
## R-HSA-2022090 1277/1278/1281/1291/1292/1293
## R-HSA-1650814 1277/1278/1281/1291/1292/1293
## R-HSA-3000480 1277/1278/1281/286133
## R-HSA-1474290 1277/1278/1281/1291/1292/1293
## R-HSA-166663 715/716/718/1675
## R-HSA-166658 715/716/718/1604/1675
## R-HSA-8957275 718/2200/2335/11167/3488/1462
## R-HSA-3000170 1277/1278/1281/2335
## R-HSA-8874081 1277/1278/1281/2335
## R-HSA-8875878 1277/1278/1281/2335
## R-HSA-2173782 1277/1278/1281/286133
## R-HSA-419037 1281/1291/1292/1293
## R-HSA-977606 715/716/718/1604
## R-HSA-9006934 71/8483/1277/1278/1281/1291/1292/1293/2335
## R-HSA-186797 1281/1291/1292/1293
## R-HSA-3000171 1277/1278/1281/2335
## R-HSA-375165 1281/1291/1292/1293
## R-HSA-6806834 1277/1278/1281/2335
## R-HSA-76009 1277/1278/2335
## R-HSA-3560782 1634/4060/1462
## R-HSA-2022923 1634/1462
## R-HSA-430116 1277/1278
## R-HSA-166786 715/716
## R-HSA-2024101 1634/1462
## R-HSA-198933 718/1277/1278/1281
## R-HSA-2214320 1277/1278
## R-HSA-75892 1277/1278
## R-HSA-2243919 1277/1278
## R-HSA-76002 1675/7123/1277/1278/2335
## R-HSA-2022870 1634/1462
## R-HSA-3560783 1634/1462
## R-HSA-3560801 1634/1462
## R-HSA-4420332 1634/1462
## R-HSA-1971475 1634/1462
## R-HSA-1592389 4313/7077
## R-HSA-6785807 1278/2335/4313
## R-HSA-114604 1277/1278
## R-HSA-6802948 71/2335
## R-HSA-5674135 71/2335
## R-HSA-1630316 1634/4060/1462
## R-HSA-114608 1675/7123/2335
## R-HSA-76005 1675/7123/2335
## R-HSA-6802946 71/2335
## R-HSA-6802949 71/2335
## R-HSA-6802955 71/2335
## R-HSA-9649948 71/2335
## R-HSA-202733 1277/1278/2335
## R-HSA-1793185 1634/1462
## R-HSA-3928665 71/4313
## R-HSA-3781865 1634/4060/1462
## R-HSA-1638091 1634/1462
## Count
## R-HSA-1474244 18
## R-HSA-3000178 11
## R-HSA-1474228 11
## R-HSA-216083 9
## R-HSA-1442490 7
## R-HSA-2129379 6
## R-HSA-8948216 6
## R-HSA-1566948 6
## R-HSA-381426 8
## R-HSA-2022090 6
## R-HSA-1650814 6
## R-HSA-3000480 4
## R-HSA-1474290 6
## R-HSA-166663 4
## R-HSA-166658 5
## R-HSA-8957275 6
## R-HSA-3000170 4
## R-HSA-8874081 4
## R-HSA-8875878 4
## R-HSA-2173782 4
## R-HSA-419037 4
## R-HSA-977606 4
## R-HSA-9006934 9
## R-HSA-186797 4
## R-HSA-3000171 4
## R-HSA-375165 4
## R-HSA-6806834 4
## R-HSA-76009 3
## R-HSA-3560782 3
## R-HSA-2022923 2
## R-HSA-430116 2
## R-HSA-166786 2
## R-HSA-2024101 2
## R-HSA-198933 4
## R-HSA-2214320 2
## R-HSA-75892 2
## R-HSA-2243919 2
## R-HSA-76002 5
## R-HSA-2022870 2
## R-HSA-3560783 2
## R-HSA-3560801 2
## R-HSA-4420332 2
## R-HSA-1971475 2
## R-HSA-1592389 2
## R-HSA-6785807 3
## R-HSA-114604 2
## R-HSA-6802948 2
## R-HSA-5674135 2
## R-HSA-1630316 3
## R-HSA-114608 3
## R-HSA-76005 3
## R-HSA-6802946 2
## R-HSA-6802949 2
## R-HSA-6802955 2
## R-HSA-9649948 2
## R-HSA-202733 3
## R-HSA-1793185 2
## R-HSA-3928665 2
## R-HSA-3781865 3
## R-HSA-1638091 2
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_10"
## [1] "APOD" "CMA1" "COL1A2" "CPA3" "CTSG" "CXCL14" "DCN" "HPGD"
## [9] "KIT" "MGP" "MS4A2" "TPSAB1" "TPSB2"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID Description GeneRatio
## GO:0004252 GO:0004252 serine-type endopeptidase activity 4/13
## GO:0008236 GO:0008236 serine-type peptidase activity 4/13
## GO:0017171 GO:0017171 serine hydrolase activity 4/13
## GO:0004175 GO:0004175 endopeptidase activity 4/13
## GO:0005201 GO:0005201 extracellular matrix structural constituent 3/13
## GO:0002020 GO:0002020 protease binding 2/13
## GO:0004955 GO:0004955 prostaglandin receptor activity 1/13
## GO:0004954 GO:0004954 prostanoid receptor activity 1/13
## GO:0048407 GO:0048407 platelet-derived growth factor binding 1/13
## BgRatio pvalue p.adjust qvalue
## GO:0004252 160/17697 4.318058e-06 0.0001333515 6.605646e-05
## GO:0008236 182/17697 7.197501e-06 0.0001333515 6.605646e-05
## GO:0017171 186/17697 7.844205e-06 0.0001333515 6.605646e-05
## GO:0004175 427/17697 2.010023e-04 0.0020910787 1.035828e-03
## GO:0005201 163/17697 2.050077e-04 0.0020910787 1.035828e-03
## GO:0002020 128/17697 3.842951e-03 0.0326650819 1.618085e-02
## GO:0004955 10/17697 7.323499e-03 0.0456343413 2.260525e-02
## GO:0004954 11/17697 8.053119e-03 0.0456343413 2.260525e-02
## GO:0048407 11/17697 8.053119e-03 0.0456343413 2.260525e-02
## geneID Count
## GO:0004252 1215/1511/7177/64499 4
## GO:0008236 1215/1511/7177/64499 4
## GO:0017171 1215/1511/7177/64499 4
## GO:0004175 1215/1511/7177/64499 4
## GO:0005201 1278/1634/4256 3
## GO:0002020 1278/3815 2
## GO:0004955 3248 1
## GO:0004954 3248 1
## GO:0048407 1278 1
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID Description
## R-HSA-1474228 R-HSA-1474228 Degradation of the extracellular matrix
## R-HSA-2022377 R-HSA-2022377 Metabolism of Angiotensinogen to Angiotensins
## R-HSA-1592389 R-HSA-1592389 Activation of Matrix Metalloproteinases
## R-HSA-1474244 R-HSA-1474244 Extracellular matrix organization
## R-HSA-2980736 R-HSA-2980736 Peptide hormone metabolism
## R-HSA-1433557 R-HSA-1433557 Signaling by SCF-KIT
## R-HSA-3000178 R-HSA-3000178 ECM proteoglycans
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-1474228 5/10 140/10654 8.719154e-08 6.800940e-06 3.395881e-06
## R-HSA-2022377 3/10 18/10654 4.823853e-07 1.881303e-05 9.393820e-06
## R-HSA-1592389 3/10 33/10654 3.201574e-06 7.616976e-05 3.803349e-05
## R-HSA-1474244 5/10 301/10654 3.906142e-06 7.616976e-05 3.803349e-05
## R-HSA-2980736 3/10 90/10654 6.702215e-05 1.045546e-03 5.220673e-04
## R-HSA-1433557 2/10 43/10654 7.014985e-04 9.119481e-03 4.553587e-03
## R-HSA-3000178 2/10 76/10654 2.177727e-03 2.426610e-02 1.211667e-02
## geneID Count
## R-HSA-1474228 1215/1278/1511/1634/7177 5
## R-HSA-2022377 1215/1359/1511 3
## R-HSA-1592389 1215/1511/7177 3
## R-HSA-1474244 1215/1278/1511/1634/7177 5
## R-HSA-2980736 1215/1359/1511 3
## R-HSA-1433557 1215/3815 2
## R-HSA-3000178 1278/1634 2
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_11"
## [1] "ACTB" "DDX21" "IFITM3" "MT1X" "MT2A" "NNMT" "PTMA" "SAA2"
## [9] "SOD2"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## [1] ID Description GeneRatio BgRatio pvalue p.adjust
## [7] qvalue geneID Count
## <0 rows> (or 0-length row.names)
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID Description
## R-HSA-5661231 R-HSA-5661231 Metallothioneins bind metals
## R-HSA-5660526 R-HSA-5660526 Response to metal ions
## R-HSA-5250924 R-HSA-5250924 B-WICH complex positively regulates rRNA expression
## R-HSA-5250913 R-HSA-5250913 Positive epigenetic regulation of rRNA expression
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-5661231 2/7 11/10654 2.029569e-05 0.001492914 0.0007416011
## R-HSA-5660526 2/7 14/10654 3.354862e-05 0.001492914 0.0007416011
## R-HSA-5250924 2/7 91/10654 1.473685e-03 0.043719322 0.0217174634
## R-HSA-5250913 2/7 106/10654 1.993292e-03 0.044350753 0.0220311248
## geneID Count
## R-HSA-5661231 4501/4502 2
## R-HSA-5660526 4501/4502 2
## R-HSA-5250924 60/9188 2
## R-HSA-5250913 60/9188 2
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## Warning in clusterProfiler::bitr(d$gene_name, fromType = "SYMBOL", toType =
## "ENTREZID", : 2.7% of input gene IDs are fail to map...## [1] "cluster_12"
## [1] "AEBP1" "APOD" "C1R" "C1S" "C3" "CCDC80" "CFD" "CFH"
## [9] "CILP" "COL1A1" "COL1A2" "COL3A1" "COL6A1" "COL6A2" "COL6A3" "CXCL12"
## [17] "DCN" "FBLN1" "FBLN2" "FBN1" "FN1" "FSTL1" "GPC3" "GPX3"
## [25] "GSN" "IGFBP6" "LUM" "MARCKS" "MFAP4" "MFAP5" "MGP" "MMP2"
## [33] "PLPP3" "TIMP2" "TNXB" "VCAN" "WISP2"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID
## GO:0005201 GO:0005201
## GO:0030020 GO:0030020
## GO:0048407 GO:0048407
## GO:0005539 GO:0005539
## GO:0005178 GO:0005178
## GO:0008201 GO:0008201
## GO:0061134 GO:0061134
## GO:0002020 GO:0002020
## GO:0019838 GO:0019838
## GO:0005518 GO:0005518
## GO:1901681 GO:1901681
## GO:0030021 GO:0030021
## GO:0001968 GO:0001968
## GO:0097493 GO:0097493
## GO:0004252 GO:0004252
## GO:0050839 GO:0050839
## GO:0008236 GO:0008236
## GO:0030414 GO:0030414
## GO:0017171 GO:0017171
## GO:0043394 GO:0043394
## GO:0016504 GO:0016504
## GO:0004866 GO:0004866
## GO:0061135 GO:0061135
## GO:0050840 GO:0050840
## GO:0004857 GO:0004857
## GO:0004175 GO:0004175
## GO:0046332 GO:0046332
## Description
## GO:0005201 extracellular matrix structural constituent
## GO:0030020 extracellular matrix structural constituent conferring tensile strength
## GO:0048407 platelet-derived growth factor binding
## GO:0005539 glycosaminoglycan binding
## GO:0005178 integrin binding
## GO:0008201 heparin binding
## GO:0061134 peptidase regulator activity
## GO:0002020 protease binding
## GO:0019838 growth factor binding
## GO:0005518 collagen binding
## GO:1901681 sulfur compound binding
## GO:0030021 extracellular matrix structural constituent conferring compression resistance
## GO:0001968 fibronectin binding
## GO:0097493 structural molecule activity conferring elasticity
## GO:0004252 serine-type endopeptidase activity
## GO:0050839 cell adhesion molecule binding
## GO:0008236 serine-type peptidase activity
## GO:0030414 peptidase inhibitor activity
## GO:0017171 serine hydrolase activity
## GO:0043394 proteoglycan binding
## GO:0016504 peptidase activator activity
## GO:0004866 endopeptidase inhibitor activity
## GO:0061135 endopeptidase regulator activity
## GO:0050840 extracellular matrix binding
## GO:0004857 enzyme inhibitor activity
## GO:0004175 endopeptidase activity
## GO:0046332 SMAD binding
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0005201 19/36 163/17697 5.355184e-30 4.230596e-28 2.423926e-28
## GO:0030020 6/36 41/17697 1.952504e-10 7.712390e-09 4.418824e-09
## GO:0048407 4/36 11/17697 4.710037e-09 1.240310e-07 7.106372e-08
## GO:0005539 8/36 229/17697 1.540728e-08 3.042938e-07 1.743455e-07
## GO:0005178 6/36 132/17697 2.490094e-07 3.934348e-06 2.254190e-06
## GO:0008201 6/36 169/17697 1.065950e-06 1.403501e-05 8.041378e-06
## GO:0061134 6/36 219/17697 4.790294e-06 5.406189e-05 3.097484e-05
## GO:0002020 5/36 128/17697 5.763819e-06 5.691771e-05 3.261108e-05
## GO:0019838 5/36 137/17697 8.032092e-06 7.050391e-05 4.039531e-05
## GO:0005518 4/36 67/17697 1.008819e-05 7.339336e-05 4.205083e-05
## GO:1901681 6/36 250/17697 1.021933e-05 7.339336e-05 4.205083e-05
## GO:0030021 3/36 22/17697 1.159310e-05 7.632125e-05 4.372837e-05
## GO:0001968 3/36 27/17697 2.186595e-05 1.328777e-04 7.613246e-05
## GO:0097493 2/36 11/17697 2.187514e-04 1.234383e-03 7.072413e-04
## GO:0004252 4/36 160/17697 3.025442e-04 1.593400e-03 9.129405e-04
## GO:0050839 6/36 499/17697 4.626610e-04 2.163451e-03 1.239552e-03
## GO:0008236 4/36 182/17697 4.929381e-04 2.163451e-03 1.239552e-03
## GO:0030414 4/36 182/17697 4.929381e-04 2.163451e-03 1.239552e-03
## GO:0017171 4/36 186/17697 5.350106e-04 2.224518e-03 1.274540e-03
## GO:0043394 2/36 36/17697 2.426870e-03 9.586138e-03 5.492391e-03
## GO:0016504 2/36 38/17697 2.701174e-03 1.016156e-02 5.822080e-03
## GO:0004866 3/36 175/17697 5.340734e-03 1.917809e-02 1.098811e-02
## GO:0061135 3/36 182/17697 5.953517e-03 1.970272e-02 1.128870e-02
## GO:0050840 2/36 57/17697 5.985636e-03 1.970272e-02 1.128870e-02
## GO:0004857 4/36 375/17697 6.843708e-03 2.162612e-02 1.239071e-02
## GO:0004175 4/36 427/17697 1.069747e-02 3.250384e-02 1.862312e-02
## GO:0046332 2/36 80/17697 1.150967e-02 3.367643e-02 1.929496e-02
## geneID
## GO:0005201 165/8483/1277/1278/1281/1291/1292/1293/1634/2192/2199/2200/2335/4060/4239/8076/4256/7148/1462
## GO:0030020 1277/1278/1281/1291/1292/1293
## GO:0048407 1277/1278/1281/1291
## GO:0005539 151887/3075/1634/2200/2335/11167/7148/1462
## GO:0005178 1281/6387/2200/2335/7077/7148
## GO:0008201 151887/3075/2200/2335/11167/7148
## GO:0061134 718/1293/2192/2335/2719/7077
## GO:0002020 1277/1278/1281/2335/7077
## GO:0019838 1277/1278/1281/1291/3489
## GO:0005518 165/1634/2335/4060
## GO:1901681 151887/3075/2200/2335/11167/7148
## GO:0030021 1634/4060/1462
## GO:0001968 151887/2192/3489
## GO:0097493 2199/2200
## GO:0004252 715/716/1675/4313
## GO:0050839 1281/6387/2200/2335/7077/7148
## GO:0008236 715/716/1675/4313
## GO:0030414 718/1293/2719/7077
## GO:0017171 715/716/1675/4313
## GO:0043394 3075/2335
## GO:0016504 2192/2335
## GO:0004866 718/1293/7077
## GO:0061135 718/1293/7077
## GO:0050840 1634/2199
## GO:0004857 718/1293/2719/7077
## GO:0004175 715/716/1675/4313
## GO:0046332 1278/1281
## Count
## GO:0005201 19
## GO:0030020 6
## GO:0048407 4
## GO:0005539 8
## GO:0005178 6
## GO:0008201 6
## GO:0061134 6
## GO:0002020 5
## GO:0019838 5
## GO:0005518 4
## GO:1901681 6
## GO:0030021 3
## GO:0001968 3
## GO:0097493 2
## GO:0004252 4
## GO:0050839 6
## GO:0008236 4
## GO:0030414 4
## GO:0017171 4
## GO:0043394 2
## GO:0016504 2
## GO:0004866 3
## GO:0061135 3
## GO:0050840 2
## GO:0004857 4
## GO:0004175 4
## GO:0046332 2
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-1474244 R-HSA-1474244
## R-HSA-3000178 R-HSA-3000178
## R-HSA-1474228 R-HSA-1474228
## R-HSA-216083 R-HSA-216083
## R-HSA-1442490 R-HSA-1442490
## R-HSA-2129379 R-HSA-2129379
## R-HSA-381426 R-HSA-381426
## R-HSA-8948216 R-HSA-8948216
## R-HSA-1566948 R-HSA-1566948
## R-HSA-2022090 R-HSA-2022090
## R-HSA-1650814 R-HSA-1650814
## R-HSA-1474290 R-HSA-1474290
## R-HSA-166663 R-HSA-166663
## R-HSA-8957275 R-HSA-8957275
## R-HSA-166658 R-HSA-166658
## R-HSA-3000170 R-HSA-3000170
## R-HSA-8874081 R-HSA-8874081
## R-HSA-3560782 R-HSA-3560782
## R-HSA-8875878 R-HSA-8875878
## R-HSA-419037 R-HSA-419037
## R-HSA-9006934 R-HSA-9006934
## R-HSA-977606 R-HSA-977606
## R-HSA-3000480 R-HSA-3000480
## R-HSA-186797 R-HSA-186797
## R-HSA-3560783 R-HSA-3560783
## R-HSA-3560801 R-HSA-3560801
## R-HSA-4420332 R-HSA-4420332
## R-HSA-3000171 R-HSA-3000171
## R-HSA-375165 R-HSA-375165
## R-HSA-1971475 R-HSA-1971475
## R-HSA-6806834 R-HSA-6806834
## R-HSA-76009 R-HSA-76009
## R-HSA-2173782 R-HSA-2173782
## R-HSA-1793185 R-HSA-1793185
## R-HSA-2022923 R-HSA-2022923
## R-HSA-1630316 R-HSA-1630316
## R-HSA-430116 R-HSA-430116
## R-HSA-1638091 R-HSA-1638091
## R-HSA-198933 R-HSA-198933
## R-HSA-166786 R-HSA-166786
## R-HSA-2024101 R-HSA-2024101
## R-HSA-3781865 R-HSA-3781865
## R-HSA-2214320 R-HSA-2214320
## R-HSA-75892 R-HSA-75892
## R-HSA-2243919 R-HSA-2243919
## R-HSA-2022870 R-HSA-2022870
## R-HSA-6785807 R-HSA-6785807
## R-HSA-1592389 R-HSA-1592389
## R-HSA-114604 R-HSA-114604
## R-HSA-76002 R-HSA-76002
## R-HSA-202733 R-HSA-202733
## R-HSA-71387 R-HSA-71387
## Description
## R-HSA-1474244 Extracellular matrix organization
## R-HSA-3000178 ECM proteoglycans
## R-HSA-1474228 Degradation of the extracellular matrix
## R-HSA-216083 Integrin cell surface interactions
## R-HSA-1442490 Collagen degradation
## R-HSA-2129379 Molecules associated with elastic fibres
## R-HSA-381426 Regulation of Insulin-like Growth Factor (IGF) transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs)
## R-HSA-8948216 Collagen chain trimerization
## R-HSA-1566948 Elastic fibre formation
## R-HSA-2022090 Assembly of collagen fibrils and other multimeric structures
## R-HSA-1650814 Collagen biosynthesis and modifying enzymes
## R-HSA-1474290 Collagen formation
## R-HSA-166663 Initial triggering of complement
## R-HSA-8957275 Post-translational protein phosphorylation
## R-HSA-166658 Complement cascade
## R-HSA-3000170 Syndecan interactions
## R-HSA-8874081 MET activates PTK2 signaling
## R-HSA-3560782 Diseases associated with glycosaminoglycan metabolism
## R-HSA-8875878 MET promotes cell motility
## R-HSA-419037 NCAM1 interactions
## R-HSA-9006934 Signaling by Receptor Tyrosine Kinases
## R-HSA-977606 Regulation of Complement cascade
## R-HSA-3000480 Scavenging by Class A Receptors
## R-HSA-186797 Signaling by PDGF
## R-HSA-3560783 Defective B4GALT7 causes EDS, progeroid type
## R-HSA-3560801 Defective B3GAT3 causes JDSSDHD
## R-HSA-4420332 Defective B3GALT6 causes EDSP2 and SEMDJL1
## R-HSA-3000171 Non-integrin membrane-ECM interactions
## R-HSA-375165 NCAM signaling for neurite out-growth
## R-HSA-1971475 A tetrasaccharide linker sequence is required for GAG synthesis
## R-HSA-6806834 Signaling by MET
## R-HSA-76009 Platelet Aggregation (Plug Formation)
## R-HSA-2173782 Binding and Uptake of Ligands by Scavenger Receptors
## R-HSA-1793185 Chondroitin sulfate/dermatan sulfate metabolism
## R-HSA-2022923 Dermatan sulfate biosynthesis
## R-HSA-1630316 Glycosaminoglycan metabolism
## R-HSA-430116 GP1b-IX-V activation signalling
## R-HSA-1638091 Heparan sulfate/heparin (HS-GAG) metabolism
## R-HSA-198933 Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell
## R-HSA-166786 Creation of C4 and C2 activators
## R-HSA-2024101 CS/DS degradation
## R-HSA-3781865 Diseases of glycosylation
## R-HSA-2214320 Anchoring fibril formation
## R-HSA-75892 Platelet Adhesion to exposed collagen
## R-HSA-2243919 Crosslinking of collagen fibrils
## R-HSA-2022870 Chondroitin sulfate biosynthesis
## R-HSA-6785807 Interleukin-4 and Interleukin-13 signaling
## R-HSA-1592389 Activation of Matrix Metalloproteinases
## R-HSA-114604 GPVI-mediated activation cascade
## R-HSA-76002 Platelet activation, signaling and aggregation
## R-HSA-202733 Cell surface interactions at the vascular wall
## R-HSA-71387 Metabolism of carbohydrates
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-1474244 18/33 301/10654 5.617676e-20 6.853565e-18 3.725406e-18
## R-HSA-3000178 11/33 76/10654 1.957921e-16 1.194332e-14 6.492055e-15
## R-HSA-1474228 11/33 140/10654 2.051014e-13 8.340790e-12 4.533820e-12
## R-HSA-216083 9/33 85/10654 2.800548e-12 8.541673e-11 4.643014e-11
## R-HSA-1442490 7/33 64/10654 7.612938e-10 1.857557e-08 1.009716e-08
## R-HSA-2129379 6/33 38/10654 1.406045e-09 2.858958e-08 1.554050e-08
## R-HSA-381426 8/33 125/10654 3.111845e-09 5.411559e-08 2.941572e-08
## R-HSA-8948216 6/33 44/10654 3.548564e-09 5.411559e-08 2.941572e-08
## R-HSA-1566948 6/33 45/10654 4.085585e-09 5.538237e-08 3.010431e-08
## R-HSA-2022090 6/33 61/10654 2.689719e-08 3.281457e-07 1.783708e-07
## R-HSA-1650814 6/33 67/10654 4.771399e-08 5.291916e-07 2.876537e-07
## R-HSA-1474290 6/33 90/10654 2.831153e-07 2.878339e-06 1.564585e-06
## R-HSA-166663 4/33 23/10654 6.479425e-07 6.080691e-06 3.305294e-06
## R-HSA-8957275 6/33 108/10654 8.366037e-07 6.889877e-06 3.745145e-06
## R-HSA-166658 5/33 58/10654 8.471161e-07 6.889877e-06 3.745145e-06
## R-HSA-3000170 4/33 27/10654 1.273027e-06 9.706832e-06 5.276363e-06
## R-HSA-8874081 4/33 30/10654 1.974922e-06 1.417297e-05 7.704031e-06
## R-HSA-3560782 4/33 41/10654 7.125040e-06 4.575026e-05 2.486856e-05
## R-HSA-8875878 4/33 41/10654 7.125040e-06 4.575026e-05 2.486856e-05
## R-HSA-419037 4/33 42/10654 7.857899e-06 4.793318e-05 2.605514e-05
## R-HSA-9006934 9/33 473/10654 9.228657e-06 5.361411e-05 2.914313e-05
## R-HSA-977606 4/33 47/10654 1.238616e-05 6.868689e-05 3.733627e-05
## R-HSA-3000480 3/33 19/10654 2.536568e-05 1.341539e-04 7.292230e-05
## R-HSA-186797 4/33 58/10654 2.876461e-05 1.341539e-04 7.292230e-05
## R-HSA-3560783 3/33 20/10654 2.977900e-05 1.341539e-04 7.292230e-05
## R-HSA-3560801 3/33 20/10654 2.977900e-05 1.341539e-04 7.292230e-05
## R-HSA-4420332 3/33 20/10654 2.977900e-05 1.341539e-04 7.292230e-05
## R-HSA-3000171 4/33 59/10654 3.078942e-05 1.341539e-04 7.292230e-05
## R-HSA-375165 4/33 63/10654 3.994722e-05 1.680538e-04 9.134937e-05
## R-HSA-1971475 3/33 26/10654 6.706182e-05 2.727181e-04 1.482419e-04
## R-HSA-6806834 4/33 79/10654 9.731075e-05 3.829649e-04 2.081690e-04
## R-HSA-76009 3/33 39/10654 2.293426e-04 8.743688e-04 4.752824e-04
## R-HSA-2173782 3/33 42/10654 2.862726e-04 1.058341e-03 5.752846e-04
## R-HSA-1793185 3/33 50/10654 4.805826e-04 1.724443e-03 9.373592e-04
## R-HSA-2022923 2/33 11/10654 5.028708e-04 1.752864e-03 9.528079e-04
## R-HSA-1630316 4/33 124/10654 5.508760e-04 1.866858e-03 1.014772e-03
## R-HSA-430116 2/33 12/10654 6.022760e-04 1.985883e-03 1.079471e-03
## R-HSA-1638091 3/33 55/10654 6.365264e-04 2.043585e-03 1.110836e-03
## R-HSA-198933 4/33 132/10654 6.972672e-04 2.181195e-03 1.185637e-03
## R-HSA-166786 2/33 14/10654 8.271976e-04 2.461417e-03 1.337958e-03
## R-HSA-2024101 2/33 14/10654 8.271976e-04 2.461417e-03 1.337958e-03
## R-HSA-3781865 4/33 143/10654 9.410190e-04 2.641330e-03 1.435753e-03
## R-HSA-2214320 2/33 15/10654 9.526108e-04 2.641330e-03 1.435753e-03
## R-HSA-75892 2/33 15/10654 9.526108e-04 2.641330e-03 1.435753e-03
## R-HSA-2243919 2/33 18/10654 1.380046e-03 3.741458e-03 2.033752e-03
## R-HSA-2022870 2/33 20/10654 1.707158e-03 4.527680e-03 2.461120e-03
## R-HSA-6785807 3/33 108/10654 4.430876e-03 1.150142e-02 6.251852e-03
## R-HSA-1592389 2/33 33/10654 4.626318e-03 1.175856e-02 6.391624e-03
## R-HSA-114604 2/33 35/10654 5.193272e-03 1.293019e-02 7.028488e-03
## R-HSA-76002 4/33 262/10654 8.347974e-03 2.036906e-02 1.107205e-02
## R-HSA-202733 3/33 137/10654 8.561817e-03 2.048121e-02 1.113301e-02
## R-HSA-71387 4/33 295/10654 1.252347e-02 2.938199e-02 1.597123e-02
## geneID
## R-HSA-1474244 1277/1278/1281/1291/1292/1293/1634/2192/2199/2200/2335/4060/4239/8076/4313/7077/7148/1462
## R-HSA-3000178 1277/1278/1281/1291/1292/1293/1634/2335/4060/7148/1462
## R-HSA-1474228 1277/1278/1281/1291/1292/1293/1634/2200/2335/4313/7077
## R-HSA-216083 1277/1278/1281/1291/1292/1293/2200/2335/4060
## R-HSA-1442490 1277/1278/1281/1291/1292/1293/4313
## R-HSA-2129379 2192/2199/2200/2335/4239/8076
## R-HSA-381426 718/2200/2335/11167/2719/3489/4313/1462
## R-HSA-8948216 1277/1278/1281/1291/1292/1293
## R-HSA-1566948 2192/2199/2200/2335/4239/8076
## R-HSA-2022090 1277/1278/1281/1291/1292/1293
## R-HSA-1650814 1277/1278/1281/1291/1292/1293
## R-HSA-1474290 1277/1278/1281/1291/1292/1293
## R-HSA-166663 715/716/718/1675
## R-HSA-8957275 718/2200/2335/11167/2719/1462
## R-HSA-166658 715/716/718/1675/3075
## R-HSA-3000170 1277/1278/1281/2335
## R-HSA-8874081 1277/1278/1281/2335
## R-HSA-3560782 1634/2719/4060/1462
## R-HSA-8875878 1277/1278/1281/2335
## R-HSA-419037 1281/1291/1292/1293
## R-HSA-9006934 8483/1277/1278/1281/1291/1292/1293/6387/2335
## R-HSA-977606 715/716/718/3075
## R-HSA-3000480 1277/1278/1281
## R-HSA-186797 1281/1291/1292/1293
## R-HSA-3560783 1634/2719/1462
## R-HSA-3560801 1634/2719/1462
## R-HSA-4420332 1634/2719/1462
## R-HSA-3000171 1277/1278/1281/2335
## R-HSA-375165 1281/1291/1292/1293
## R-HSA-1971475 1634/2719/1462
## R-HSA-6806834 1277/1278/1281/2335
## R-HSA-76009 1277/1278/2335
## R-HSA-2173782 1277/1278/1281
## R-HSA-1793185 1634/2719/1462
## R-HSA-2022923 1634/1462
## R-HSA-1630316 1634/2719/4060/1462
## R-HSA-430116 1277/1278
## R-HSA-1638091 1634/2719/1462
## R-HSA-198933 718/1277/1278/1281
## R-HSA-166786 715/716
## R-HSA-2024101 1634/1462
## R-HSA-3781865 1634/2719/4060/1462
## R-HSA-2214320 1277/1278
## R-HSA-75892 1277/1278
## R-HSA-2243919 1277/1278
## R-HSA-2022870 1634/1462
## R-HSA-6785807 1278/2335/4313
## R-HSA-1592389 4313/7077
## R-HSA-114604 1277/1278
## R-HSA-76002 1675/1277/1278/2335
## R-HSA-202733 1277/1278/2335
## R-HSA-71387 1634/2719/4060/1462
## Count
## R-HSA-1474244 18
## R-HSA-3000178 11
## R-HSA-1474228 11
## R-HSA-216083 9
## R-HSA-1442490 7
## R-HSA-2129379 6
## R-HSA-381426 8
## R-HSA-8948216 6
## R-HSA-1566948 6
## R-HSA-2022090 6
## R-HSA-1650814 6
## R-HSA-1474290 6
## R-HSA-166663 4
## R-HSA-8957275 6
## R-HSA-166658 5
## R-HSA-3000170 4
## R-HSA-8874081 4
## R-HSA-3560782 4
## R-HSA-8875878 4
## R-HSA-419037 4
## R-HSA-9006934 9
## R-HSA-977606 4
## R-HSA-3000480 3
## R-HSA-186797 4
## R-HSA-3560783 3
## R-HSA-3560801 3
## R-HSA-4420332 3
## R-HSA-3000171 4
## R-HSA-375165 4
## R-HSA-1971475 3
## R-HSA-6806834 4
## R-HSA-76009 3
## R-HSA-2173782 3
## R-HSA-1793185 3
## R-HSA-2022923 2
## R-HSA-1630316 4
## R-HSA-430116 2
## R-HSA-1638091 3
## R-HSA-198933 4
## R-HSA-166786 2
## R-HSA-2024101 2
## R-HSA-3781865 4
## R-HSA-2214320 2
## R-HSA-75892 2
## R-HSA-2243919 2
## R-HSA-2022870 2
## R-HSA-6785807 3
## R-HSA-1592389 2
## R-HSA-114604 2
## R-HSA-76002 4
## R-HSA-202733 3
## R-HSA-71387 4
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_13"
## [1] "ACTB" "B2M" "CCL5" "CD74" "GNLY" "HLA-A" "HLA-B" "NKG7"
## [9] "TMSB10"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID Description GeneRatio
## GO:0050998 GO:0050998 nitric-oxide synthase binding 2/8
## GO:0042605 GO:0042605 peptide antigen binding 2/8
## GO:0042277 GO:0042277 peptide binding 3/8
## GO:0033218 GO:0033218 amide binding 3/8
## GO:0003823 GO:0003823 antigen binding 2/8
## GO:0030957 GO:0030957 Tat protein binding 1/8
## GO:0099186 GO:0099186 structural constituent of postsynapse 1/8
## GO:0016004 GO:0016004 phospholipase activator activity 1/8
## GO:0060229 GO:0060229 lipase activator activity 1/8
## GO:0023026 GO:0023026 MHC class II protein complex binding 1/8
## GO:0098918 GO:0098918 structural constituent of synapse 1/8
## GO:0030296 GO:0030296 protein tyrosine kinase activator activity 1/8
## GO:0044183 GO:0044183 protein binding involved in protein folding 1/8
## GO:0023023 GO:0023023 MHC protein complex binding 1/8
## GO:0004435 GO:0004435 phosphatidylinositol phospholipase C activity 1/8
## GO:0003785 GO:0003785 actin monomer binding 1/8
## GO:0004629 GO:0004629 phospholipase C activity 1/8
## GO:0048019 GO:0048019 receptor antagonist activity 1/8
## GO:0042056 GO:0042056 chemoattractant activity 1/8
## GO:0042287 GO:0042287 MHC protein binding 1/8
## GO:0030547 GO:0030547 receptor inhibitor activity 1/8
## GO:0019894 GO:0019894 kinesin binding 1/8
## GO:0048020 GO:0048020 CCR chemokine receptor binding 1/8
## GO:0048156 GO:0048156 tau protein binding 1/8
## GO:0008009 GO:0008009 chemokine activity 1/8
## GO:0031492 GO:0031492 nucleosomal DNA binding 1/8
## GO:0043621 GO:0043621 protein self-association 1/8
## BgRatio pvalue p.adjust qvalue geneID Count
## GO:0050998 14/17697 1.622842e-05 0.0008114212 0.0002220732 60/972 2
## GO:0042605 31/17697 8.260728e-05 0.0020651819 0.0005652077 3105/3106 2
## GO:0042277 295/17697 2.413230e-04 0.0040220492 0.0011007714 972/3105/3106 3
## GO:0033218 356/17697 4.193453e-04 0.0052418161 0.0014346023 972/3105/3106 3
## GO:0003823 160/17697 2.194675e-03 0.0219467511 0.0060064793 3105/3106 2
## GO:0030957 10/17697 4.512501e-03 0.0338303721 0.0092588387 60 1
## GO:0099186 11/17697 4.962769e-03 0.0338303721 0.0092588387 60 1
## GO:0016004 12/17697 5.412860e-03 0.0338303721 0.0092588387 6352 1
## GO:0060229 14/17697 6.312506e-03 0.0348210917 0.0095299830 6352 1
## GO:0023026 16/17697 7.211440e-03 0.0348210917 0.0095299830 972 1
## GO:0098918 17/17697 7.660640e-03 0.0348210917 0.0095299830 60 1
## GO:0030296 19/17697 8.558507e-03 0.0356604461 0.0097597010 6352 1
## GO:0044183 23/17697 1.035211e-02 0.0370297569 0.0101344598 972 1
## GO:0023023 25/17697 1.124784e-02 0.0370297569 0.0101344598 972 1
## GO:0004435 26/17697 1.169545e-02 0.0370297569 0.0101344598 6352 1
## GO:0003785 28/17697 1.259012e-02 0.0370297569 0.0101344598 9168 1
## GO:0004629 28/17697 1.259012e-02 0.0370297569 0.0101344598 6352 1
## GO:0048019 33/17697 1.482370e-02 0.0411769346 0.0112694768 6352 1
## GO:0042056 38/17697 1.705285e-02 0.0419078166 0.0114695077 6352 1
## GO:0042287 40/17697 1.794328e-02 0.0419078166 0.0114695077 972 1
## GO:0030547 41/17697 1.838823e-02 0.0419078166 0.0114695077 6352 1
## GO:0019894 42/17697 1.883300e-02 0.0419078166 0.0114695077 60 1
## GO:0048020 43/17697 1.927760e-02 0.0419078166 0.0114695077 6352 1
## GO:0048156 45/17697 2.016626e-02 0.0420130377 0.0114983051 60 1
## GO:0008009 49/17697 2.194147e-02 0.0438829391 0.0120100675 6352 1
## GO:0031492 55/17697 2.459901e-02 0.0463728123 0.0126915065 60 1
## GO:0043621 56/17697 2.504132e-02 0.0463728123 0.0126915065 6352 1
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-1236977 R-HSA-1236977
## R-HSA-983170 R-HSA-983170
## R-HSA-1236974 R-HSA-1236974
## R-HSA-877300 R-HSA-877300
## R-HSA-1236975 R-HSA-1236975
## R-HSA-198933 R-HSA-198933
## R-HSA-913531 R-HSA-913531
## R-HSA-909733 R-HSA-909733
## R-HSA-983169 R-HSA-983169
## Description
## R-HSA-1236977 Endosomal/Vacuolar pathway
## R-HSA-983170 Antigen Presentation: Folding, assembly and peptide loading of class I MHC
## R-HSA-1236974 ER-Phagosome pathway
## R-HSA-877300 Interferon gamma signaling
## R-HSA-1236975 Antigen processing-Cross presentation
## R-HSA-198933 Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell
## R-HSA-913531 Interferon Signaling
## R-HSA-909733 Interferon alpha/beta signaling
## R-HSA-983169 Class I MHC mediated antigen processing & presentation
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-1236977 3/7 11/10654 2.859621e-08 2.516467e-06 1.324456e-06
## R-HSA-983170 3/7 25/10654 3.970436e-07 1.746992e-05 9.194695e-06
## R-HSA-1236974 3/7 83/10654 1.560343e-05 4.577006e-04 2.408950e-04
## R-HSA-877300 3/7 92/10654 2.127201e-05 4.666826e-04 2.456224e-04
## R-HSA-1236975 3/7 99/10654 2.651606e-05 4.666826e-04 2.456224e-04
## R-HSA-198933 3/7 132/10654 6.274825e-05 9.203077e-04 4.843725e-04
## R-HSA-913531 3/7 199/10654 2.125815e-04 2.672453e-03 1.406554e-03
## R-HSA-909733 2/7 69/10654 8.501123e-04 9.351236e-03 4.921703e-03
## R-HSA-983169 3/7 371/10654 1.320576e-03 1.291230e-02 6.795947e-03
## geneID Count
## R-HSA-1236977 567/3105/3106 3
## R-HSA-983170 567/3105/3106 3
## R-HSA-1236974 567/3105/3106 3
## R-HSA-877300 567/3105/3106 3
## R-HSA-1236975 567/3105/3106 3
## R-HSA-198933 567/3105/3106 3
## R-HSA-913531 567/3105/3106 3
## R-HSA-909733 3105/3106 2
## R-HSA-983169 567/3105/3106 3
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## Warning in clusterProfiler::bitr(d$gene_name, fromType = "SYMBOL", toType =
## "ENTREZID", : 2.78% of input gene IDs are fail to map...## [1] "cluster_14"
## [1] "A2M" "ACKR1" "ACTA2" "ACTG1" "ACTN4" "ADGRL4"
## [7] "AEBP1" "APOD" "APP" "AQP1" "B2M" "BST2"
## [13] "C1R" "C1S" "C2orf40" "C3" "CALD1" "CAVIN1"
## [19] "CD34" "CD44" "CD59" "CD74" "CD93" "CLDN5"
## [25] "COL14A1" "CRIP2" "CST3" "CTGF" "CTSC" "CXCL12"
## [31] "CXCL14" "EEF1B2" "EEF1D" "EGFL7" "ENG" "EPAS1"
## [37] "FAU" "FKBP1A" "FLNA" "FN1" "FOS" "FOXP1"
## [43] "FXYD5" "GGT5" "GIMAP4" "GNAI2" "GSN" "H3F3B"
## [49] "HLA-A" "HLA-B" "HLA-C" "HLA-DMA" "HLA-DPA1" "HLA-DPB1"
## [55] "HLA-DQB1" "HLA-DRA" "HLA-DRB1" "HLA-E" "HSPB1" "IFI16"
## [61] "IFI27" "IFITM2" "IFITM3" "IGFBP4" "IGFBP5" "IGFBP7"
## [67] "KCTD12" "KLF2" "LIFR" "LMOD1" "LRRFIP1" "LY6E"
## [73] "MGP" "MYH9" "NOP53" "NOTCH3" "NR2F2" "PECAM1"
## [79] "PFDN5" "PLAT" "PLVAP" "PRRX1" "PTMS" "RACK1"
## [85] "RAMP2" "RAMP3" "RNASE1" "S100A4" "S100A6" "SAMHD1"
## [91] "SPARCL1" "STEAP4" "SYNE2" "SYNPO2" "TAGLN" "TCF4"
## [97] "TGFBR2" "TIMP3" "TM4SF1" "TMSB10" "TMSB4X" "TNFSF10"
## [103] "TSPAN7" "TXNIP" "UBA52" "VWF" "ZFP36L1" "ZFP36L2"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID
## GO:0042605 GO:0042605
## GO:0042277 GO:0042277
## GO:0033218 GO:0033218
## GO:0003823 GO:0003823
## GO:0003779 GO:0003779
## GO:0023026 GO:0023026
## GO:0005518 GO:0005518
## GO:0061134 GO:0061134
## GO:0023023 GO:0023023
## GO:0019838 GO:0019838
## GO:0032395 GO:0032395
## GO:0034713 GO:0034713
## GO:0050839 GO:0050839
## GO:0016504 GO:0016504
## GO:0005523 GO:0005523
## GO:0019955 GO:0019955
## GO:0004857 GO:0004857
## GO:0005201 GO:0005201
## GO:0004866 GO:0004866
## GO:0005520 GO:0005520
## GO:0030414 GO:0030414
## GO:0061135 GO:0061135
## GO:0045296 GO:0045296
## GO:0002020 GO:0002020
## GO:0005178 GO:0005178
## GO:0051015 GO:0051015
## GO:0046332 GO:0046332
## GO:0005126 GO:0005126
## GO:0031994 GO:0031994
## GO:0048185 GO:0048185
## GO:0005160 GO:0005160
## GO:0008191 GO:0008191
## GO:0048306 GO:0048306
## GO:0016505 GO:0016505
## GO:0003746 GO:0003746
## GO:0001848 GO:0001848
## Description GeneRatio
## GO:0042605 peptide antigen binding 9/102
## GO:0042277 peptide binding 13/102
## GO:0033218 amide binding 13/102
## GO:0003823 antigen binding 9/102
## GO:0003779 actin binding 13/102
## GO:0023026 MHC class II protein complex binding 4/102
## GO:0005518 collagen binding 6/102
## GO:0061134 peptidase regulator activity 9/102
## GO:0023023 MHC protein complex binding 4/102
## GO:0019838 growth factor binding 7/102
## GO:0032395 MHC class II receptor activity 3/102
## GO:0034713 type I transforming growth factor beta receptor binding 3/102
## GO:0050839 cell adhesion molecule binding 12/102
## GO:0016504 peptidase activator activity 4/102
## GO:0005523 tropomyosin binding 3/102
## GO:0019955 cytokine binding 6/102
## GO:0004857 enzyme inhibitor activity 9/102
## GO:0005201 extracellular matrix structural constituent 6/102
## GO:0004866 endopeptidase inhibitor activity 6/102
## GO:0005520 insulin-like growth factor binding 3/102
## GO:0030414 peptidase inhibitor activity 6/102
## GO:0061135 endopeptidase regulator activity 6/102
## GO:0045296 cadherin binding 8/102
## GO:0002020 protease binding 5/102
## GO:0005178 integrin binding 5/102
## GO:0051015 actin filament binding 6/102
## GO:0046332 SMAD binding 4/102
## GO:0005126 cytokine receptor binding 7/102
## GO:0031994 insulin-like growth factor I binding 2/102
## GO:0048185 activin binding 2/102
## GO:0005160 transforming growth factor beta receptor binding 3/102
## GO:0008191 metalloendopeptidase inhibitor activity 2/102
## GO:0048306 calcium-dependent protein binding 3/102
## GO:0016505 peptidase activator activity involved in apoptotic process 2/102
## GO:0003746 translation elongation factor activity 2/102
## GO:0001848 complement binding 2/102
## BgRatio pvalue p.adjust qvalue
## GO:0042605 31/17697 8.884548e-14 2.398828e-11 1.945248e-11
## GO:0042277 295/17697 1.463811e-08 1.976145e-06 1.602488e-06
## GO:0033218 356/17697 1.323549e-07 1.191194e-05 9.659587e-06
## GO:0003823 160/17697 3.599187e-07 2.429451e-05 1.970081e-05
## GO:0003779 431/17697 1.158673e-06 6.256833e-05 5.073767e-05
## GO:0023026 16/17697 1.794941e-06 8.077235e-05 6.549961e-05
## GO:0005518 67/17697 2.373086e-06 9.153333e-05 7.422586e-05
## GO:0061134 219/17697 4.883831e-06 1.648293e-04 1.336627e-04
## GO:0023023 25/17697 1.198809e-05 3.596428e-04 2.916402e-04
## GO:0019838 137/17697 1.430767e-05 3.863072e-04 3.132627e-04
## GO:0032395 10/17697 2.166220e-05 5.317086e-04 4.311711e-04
## GO:0034713 11/17697 2.966074e-05 6.212546e-04 5.037854e-04
## GO:0050839 499/17697 2.991226e-05 6.212546e-04 5.037854e-04
## GO:0016504 38/17697 6.604305e-05 1.273687e-03 1.032854e-03
## GO:0005523 15/17697 8.043052e-05 1.447749e-03 1.174003e-03
## GO:0019955 128/17697 9.718037e-05 1.639919e-03 1.329837e-03
## GO:0004857 375/17697 3.164793e-04 5.026436e-03 4.076018e-03
## GO:0005201 163/17697 3.616957e-04 5.425435e-03 4.399573e-03
## GO:0004866 175/17697 5.274435e-04 7.403680e-03 6.003764e-03
## GO:0005520 28/17697 5.484208e-04 7.403680e-03 6.003764e-03
## GO:0030414 182/17697 6.483435e-04 7.712459e-03 6.254158e-03
## GO:0061135 182/17697 6.483435e-04 7.712459e-03 6.254158e-03
## GO:0045296 331/17697 6.569873e-04 7.712459e-03 6.254158e-03
## GO:0002020 128/17697 8.705061e-04 9.793194e-03 7.941459e-03
## GO:0005178 132/17697 9.994949e-04 1.043900e-02 8.465152e-03
## GO:0051015 198/17697 1.005237e-03 1.043900e-02 8.465152e-03
## GO:0046332 80/17697 1.176003e-03 1.176003e-02 9.536397e-03
## GO:0005126 286/17697 1.336294e-03 1.288569e-02 1.044922e-02
## GO:0031994 12/17697 2.090882e-03 1.946683e-02 1.578597e-02
## GO:0048185 14/17697 2.861299e-03 2.575169e-02 2.088246e-02
## GO:0005160 51/17697 3.167501e-03 2.758791e-02 2.237148e-02
## GO:0008191 16/17697 3.744917e-03 3.159774e-02 2.562312e-02
## GO:0048306 61/17697 5.251524e-03 4.190406e-02 3.398068e-02
## GO:0016505 19/17697 5.276807e-03 4.190406e-02 3.398068e-02
## GO:0003746 20/17697 5.841192e-03 4.506063e-02 3.654039e-02
## GO:0001848 21/17697 6.431921e-03 4.823941e-02 3.911812e-02
## geneID
## GO:0042605 3105/3106/3107/3113/3115/3119/3122/3123/3133
## GO:0042277 972/1471/3105/3106/3107/3113/3115/3119/3122/3123/3133/10266/10268
## GO:0033218 972/1471/3105/3106/3107/3113/3115/3119/3122/3123/3133/10266/10268
## GO:0003823 3105/3106/3107/3113/3115/3119/3122/3123/3133
## GO:0003779 81/800/2316/53827/2934/25802/4627/6275/23224/171024/6876/9168/7114
## GO:0023026 972/3108/3122/3123
## GO:0005518 165/960/7373/2335/8404/7450
## GO:0061134 2/351/684/718/1471/1075/2335/10399/7078
## GO:0023023 972/3108/3122/3123
## GO:0019838 2/2022/3487/3488/3490/3977/7048
## GO:0032395 3113/3119/3122
## GO:0034713 2022/2280/7048
## GO:0050839 81/800/6387/1936/2316/2335/53827/9208/4627/4854/10399/7450
## GO:0016504 351/1075/2335/10399
## GO:0005523 800/25802/6277
## GO:0019955 2/2532/972/2022/3977/7048
## GO:0004857 2/351/684/718/1471/3315/10399/7078/10628
## GO:0005201 165/7373/2335/3490/4256/7450
## GO:0004866 2/351/684/718/1471/7078
## GO:0005520 3487/3488/3490
## GO:0030414 2/351/684/718/1471/7078
## GO:0061135 2/351/684/718/1471/7078
## GO:0045296 800/1936/2316/53827/9208/4627/4854/10399
## GO:0002020 2/1471/2335/7078/7450
## GO:0005178 81/6387/2335/4627/7450
## GO:0051015 81/2316/2934/4627/23224/6876
## GO:0046332 2280/2316/2353/7048
## GO:0005126 6387/9547/2022/2280/3977/7048/8743
## GO:0031994 3487/3488
## GO:0048185 2022/2280
## GO:0005160 2022/2280/7048
## GO:0008191 684/7078
## GO:0048306 2/6275/6277
## GO:0016505 1075/10399
## GO:0003746 1933/1936
## GO:0001848 966/22918
## Count
## GO:0042605 9
## GO:0042277 13
## GO:0033218 13
## GO:0003823 9
## GO:0003779 13
## GO:0023026 4
## GO:0005518 6
## GO:0061134 9
## GO:0023023 4
## GO:0019838 7
## GO:0032395 3
## GO:0034713 3
## GO:0050839 12
## GO:0016504 4
## GO:0005523 3
## GO:0019955 6
## GO:0004857 9
## GO:0005201 6
## GO:0004866 6
## GO:0005520 3
## GO:0030414 6
## GO:0061135 6
## GO:0045296 8
## GO:0002020 5
## GO:0005178 5
## GO:0051015 6
## GO:0046332 4
## GO:0005126 7
## GO:0031994 2
## GO:0048185 2
## GO:0005160 3
## GO:0008191 2
## GO:0048306 3
## GO:0016505 2
## GO:0003746 2
## GO:0001848 2
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-913531 R-HSA-913531
## R-HSA-877300 R-HSA-877300
## R-HSA-909733 R-HSA-909733
## R-HSA-1236977 R-HSA-1236977
## R-HSA-202430 R-HSA-202430
## R-HSA-114608 R-HSA-114608
## R-HSA-202427 R-HSA-202427
## R-HSA-389948 R-HSA-389948
## R-HSA-76005 R-HSA-76005
## R-HSA-983170 R-HSA-983170
## R-HSA-8957275 R-HSA-8957275
## R-HSA-381426 R-HSA-381426
## R-HSA-202433 R-HSA-202433
## R-HSA-76002 R-HSA-76002
## R-HSA-1236974 R-HSA-1236974
## R-HSA-2132295 R-HSA-2132295
## R-HSA-198933 R-HSA-198933
## R-HSA-202424 R-HSA-202424
## R-HSA-1236975 R-HSA-1236975
## R-HSA-388841 R-HSA-388841
## R-HSA-2173791 R-HSA-2173791
## R-HSA-6798695 R-HSA-6798695
## R-HSA-202403 R-HSA-202403
## R-HSA-977606 R-HSA-977606
## R-HSA-166663 R-HSA-166663
## R-HSA-166658 R-HSA-166658
## R-HSA-977225 R-HSA-977225
## R-HSA-2173789 R-HSA-2173789
## R-HSA-419812 R-HSA-419812
## R-HSA-6802948 R-HSA-6802948
## Description
## R-HSA-913531 Interferon Signaling
## R-HSA-877300 Interferon gamma signaling
## R-HSA-909733 Interferon alpha/beta signaling
## R-HSA-1236977 Endosomal/Vacuolar pathway
## R-HSA-202430 Translocation of ZAP-70 to Immunological synapse
## R-HSA-114608 Platelet degranulation
## R-HSA-202427 Phosphorylation of CD3 and TCR zeta chains
## R-HSA-389948 PD-1 signaling
## R-HSA-76005 Response to elevated platelet cytosolic Ca2+
## R-HSA-983170 Antigen Presentation: Folding, assembly and peptide loading of class I MHC
## R-HSA-8957275 Post-translational protein phosphorylation
## R-HSA-381426 Regulation of Insulin-like Growth Factor (IGF) transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs)
## R-HSA-202433 Generation of second messenger molecules
## R-HSA-76002 Platelet activation, signaling and aggregation
## R-HSA-1236974 ER-Phagosome pathway
## R-HSA-2132295 MHC class II antigen presentation
## R-HSA-198933 Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell
## R-HSA-202424 Downstream TCR signaling
## R-HSA-1236975 Antigen processing-Cross presentation
## R-HSA-388841 Costimulation by the CD28 family
## R-HSA-2173791 TGF-beta receptor signaling in EMT (epithelial to mesenchymal transition)
## R-HSA-6798695 Neutrophil degranulation
## R-HSA-202403 TCR signaling
## R-HSA-977606 Regulation of Complement cascade
## R-HSA-166663 Initial triggering of complement
## R-HSA-166658 Complement cascade
## R-HSA-977225 Amyloid fiber formation
## R-HSA-2173789 TGF-beta receptor signaling activates SMADs
## R-HSA-419812 Calcitonin-like ligand receptors
## R-HSA-6802948 Signaling by high-kinase activity BRAF mutants
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-913531 18/80 199/10654 4.555126e-15 2.541760e-12 2.220025e-12
## R-HSA-877300 11/80 92/10654 6.940172e-11 1.936308e-08 1.691210e-08
## R-HSA-909733 9/80 69/10654 1.884905e-09 3.505924e-07 3.062144e-07
## R-HSA-1236977 5/80 11/10654 9.381387e-09 1.308704e-06 1.143048e-06
## R-HSA-202430 5/80 19/10654 2.252663e-07 2.513971e-05 2.195753e-05
## R-HSA-114608 9/80 129/10654 4.745985e-07 3.995630e-05 3.489863e-05
## R-HSA-202427 5/80 22/10654 5.012439e-07 3.995630e-05 3.489863e-05
## R-HSA-389948 5/80 23/10654 6.367250e-07 4.064199e-05 3.549753e-05
## R-HSA-76005 9/80 134/10654 6.555160e-07 4.064199e-05 3.549753e-05
## R-HSA-983170 5/80 25/10654 9.936085e-07 5.544335e-05 4.842534e-05
## R-HSA-8957275 8/80 108/10654 1.359570e-06 6.896727e-05 6.023740e-05
## R-HSA-381426 8/80 125/10654 4.099809e-06 1.817701e-04 1.587617e-04
## R-HSA-202433 5/80 33/10654 4.234788e-06 1.817701e-04 1.587617e-04
## R-HSA-76002 10/80 262/10654 2.465542e-05 9.826948e-04 8.583054e-04
## R-HSA-1236974 6/80 83/10654 3.532910e-05 1.269890e-03 1.109147e-03
## R-HSA-2132295 7/80 123/10654 3.641260e-05 1.269890e-03 1.109147e-03
## R-HSA-198933 7/80 132/10654 5.724084e-05 1.878846e-03 1.641022e-03
## R-HSA-202424 6/80 98/10654 9.011388e-05 2.793530e-03 2.439926e-03
## R-HSA-1236975 6/80 99/10654 9.535895e-05 2.800542e-03 2.446050e-03
## R-HSA-388841 5/80 70/10654 1.738547e-04 4.850545e-03 4.236564e-03
## R-HSA-2173791 3/80 16/10654 2.127857e-04 5.654020e-03 4.938335e-03
## R-HSA-6798695 12/80 480/10654 2.247466e-04 5.700392e-03 4.978837e-03
## R-HSA-202403 6/80 119/10654 2.622198e-04 6.361680e-03 5.556419e-03
## R-HSA-977606 4/80 47/10654 4.113985e-04 9.565016e-03 8.354277e-03
## R-HSA-166663 3/80 23/10654 6.479216e-04 1.446161e-02 1.263106e-02
## R-HSA-166658 4/80 58/10654 9.193237e-04 1.973010e-02 1.723267e-02
## R-HSA-977225 5/80 109/10654 1.336524e-03 2.762149e-02 2.412517e-02
## R-HSA-2173789 3/80 32/10654 1.728566e-03 3.444786e-02 3.008745e-02
## R-HSA-419812 2/80 10/10654 2.409767e-03 4.529566e-02 3.956214e-02
## R-HSA-6802948 3/80 36/10654 2.435251e-03 4.529566e-02 3.956214e-02
## geneID
## R-HSA-913531 567/684/960/2316/3105/3106/3107/3113/3115/3119/3122/3123/3133/3429/10581/10410/25939/7311
## R-HSA-877300 567/960/3105/3106/3107/3113/3115/3119/3122/3123/3133
## R-HSA-909733 684/3105/3106/3107/3133/3429/10581/10410/25939
## R-HSA-1236977 567/3105/3106/3107/3133
## R-HSA-202430 3113/3115/3119/3122/3123
## R-HSA-114608 2/81/351/2316/2335/5175/7078/7114/7450
## R-HSA-202427 3113/3115/3119/3122/3123
## R-HSA-389948 3113/3115/3119/3122/3123
## R-HSA-76005 2/81/351/2316/2335/5175/7078/7114/7450
## R-HSA-983170 567/3105/3106/3107/3133
## R-HSA-8957275 351/718/1471/2335/3487/3488/3490/8404
## R-HSA-381426 351/718/1471/2335/3487/3488/3490/8404
## R-HSA-202433 3113/3115/3119/3122/3123
## R-HSA-76002 2/81/351/2316/2335/2771/5175/7078/7114/7450
## R-HSA-1236974 567/3105/3106/3107/3133/7311
## R-HSA-2132295 972/1075/3113/3115/3119/3122/3123
## R-HSA-198933 567/718/947/3105/3106/3107/3133
## R-HSA-202424 3113/3115/3119/3122/3123/7311
## R-HSA-1236975 567/3105/3106/3107/3133/7311
## R-HSA-388841 3113/3115/3119/3122/3123
## R-HSA-2173791 2280/7048/7311
## R-HSA-6798695 567/684/718/960/966/22918/1471/1075/2934/3106/3107/5175
## R-HSA-202403 3113/3115/3119/3122/3123/7311
## R-HSA-977606 715/716/718/966
## R-HSA-166663 715/716/718
## R-HSA-166658 715/716/718/966
## R-HSA-977225 351/567/1471/2934/7311
## R-HSA-2173789 2280/7048/7311
## R-HSA-419812 10266/10268
## R-HSA-6802948 71/2335/7450
## Count
## R-HSA-913531 18
## R-HSA-877300 11
## R-HSA-909733 9
## R-HSA-1236977 5
## R-HSA-202430 5
## R-HSA-114608 9
## R-HSA-202427 5
## R-HSA-389948 5
## R-HSA-76005 9
## R-HSA-983170 5
## R-HSA-8957275 8
## R-HSA-381426 8
## R-HSA-202433 5
## R-HSA-76002 10
## R-HSA-1236974 6
## R-HSA-2132295 7
## R-HSA-198933 7
## R-HSA-202424 6
## R-HSA-1236975 6
## R-HSA-388841 5
## R-HSA-2173791 3
## R-HSA-6798695 12
## R-HSA-202403 6
## R-HSA-977606 4
## R-HSA-166663 3
## R-HSA-166658 4
## R-HSA-977225 5
## R-HSA-2173789 3
## R-HSA-419812 2
## R-HSA-6802948 3
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_15"
## [1] "B2M" "LYZ" "S100A8" "S100A9"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID Description
## GO:0050786 GO:0050786 RAGE receptor binding
## GO:0035325 GO:0035325 Toll-like receptor binding
## GO:0036041 GO:0036041 long-chain fatty acid binding
## GO:1901567 GO:1901567 fatty acid derivative binding
## GO:0005504 GO:0005504 fatty acid binding
## GO:0033293 GO:0033293 monocarboxylic acid binding
## GO:0031406 GO:0031406 carboxylic acid binding
## GO:0043177 GO:0043177 organic acid binding
## GO:0008017 GO:0008017 microtubule binding
## GO:0015631 GO:0015631 tubulin binding
## GO:0061783 GO:0061783 peptidoglycan muralytic activity
## GO:0016209 GO:0016209 antioxidant activity
## GO:0004553 GO:0004553 hydrolase activity, hydrolyzing O-glycosyl compounds
## GO:0016798 GO:0016798 hydrolase activity, acting on glycosyl bonds
## GeneRatio BgRatio pvalue p.adjust qvalue geneID Count
## GO:0050786 2/4 11/17697 2.106079e-06 1.625780e-05 NA 6279/6280 2
## GO:0035325 2/4 12/17697 2.527104e-06 1.625780e-05 NA 6279/6280 2
## GO:0036041 2/4 14/17697 3.483815e-06 1.625780e-05 NA 6279/6280 2
## GO:1901567 2/4 28/17697 1.445596e-05 5.059587e-05 NA 6279/6280 2
## GO:0005504 2/4 34/17697 2.144478e-05 6.004538e-05 NA 6279/6280 2
## GO:0033293 2/4 64/17697 7.688931e-05 1.794084e-04 NA 6279/6280 2
## GO:0031406 2/4 193/17697 6.997852e-04 1.380807e-03 NA 6279/6280 2
## GO:0043177 2/4 205/17697 7.890327e-04 1.380807e-03 NA 6279/6280 2
## GO:0008017 2/4 246/17697 1.133602e-03 1.763380e-03 NA 6279/6280 2
## GO:0015631 2/4 336/17697 2.102664e-03 2.943729e-03 NA 6279/6280 2
## GO:0061783 1/4 13/17697 2.935364e-03 3.735917e-03 NA 4069 1
## GO:0016209 1/4 86/17697 1.929871e-02 2.251516e-02 NA 6280 1
## GO:0004553 1/4 94/17697 2.107963e-02 2.270114e-02 NA 4069 1
## GO:0016798 1/4 117/17697 2.618626e-02 2.618626e-02 NA 4069 1
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-6803157 R-HSA-6803157
## R-HSA-6798695 R-HSA-6798695
## R-HSA-5686938 R-HSA-5686938
## R-HSA-5668599 R-HSA-5668599
## R-HSA-977225 R-HSA-977225
## R-HSA-168898 R-HSA-168898
## R-HSA-1236977 R-HSA-1236977
## R-HSA-195258 R-HSA-195258
## R-HSA-164938 R-HSA-164938
## R-HSA-983170 R-HSA-983170
## R-HSA-164952 R-HSA-164952
## R-HSA-194315 R-HSA-194315
## R-HSA-2424491 R-HSA-2424491
## R-HSA-2172127 R-HSA-2172127
## R-HSA-1236974 R-HSA-1236974
## R-HSA-877300 R-HSA-877300
## R-HSA-1236975 R-HSA-1236975
## Description
## R-HSA-6803157 Antimicrobial peptides
## R-HSA-6798695 Neutrophil degranulation
## R-HSA-5686938 Regulation of TLR by endogenous ligand
## R-HSA-5668599 RHO GTPases Activate NADPH Oxidases
## R-HSA-977225 Amyloid fiber formation
## R-HSA-168898 Toll-like Receptor Cascades
## R-HSA-1236977 Endosomal/Vacuolar pathway
## R-HSA-195258 RHO GTPase Effectors
## R-HSA-164938 Nef-mediates down modulation of cell surface receptors by recruiting them to clathrin adapters
## R-HSA-983170 Antigen Presentation: Folding, assembly and peptide loading of class I MHC
## R-HSA-164952 The role of Nef in HIV-1 replication and disease pathogenesis
## R-HSA-194315 Signaling by Rho GTPases
## R-HSA-2424491 DAP12 signaling
## R-HSA-2172127 DAP12 interactions
## R-HSA-1236974 ER-Phagosome pathway
## R-HSA-877300 Interferon gamma signaling
## R-HSA-1236975 Antigen processing-Cross presentation
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-6803157 3/4 97/10654 2.907546e-06 0.0000468182 8.570837e-06
## R-HSA-6798695 4/4 480/10654 4.071148e-06 0.0000468182 8.570837e-06
## R-HSA-5686938 2/4 19/10654 1.804131e-05 0.0001383167 2.532114e-05
## R-HSA-5668599 2/4 24/10654 2.910107e-05 0.0001673312 3.063271e-05
## R-HSA-977225 2/4 109/10654 6.140205e-04 0.0028244945 5.170699e-04
## R-HSA-168898 2/4 155/10654 1.237845e-03 0.0047450743 8.686635e-04
## R-HSA-1236977 1/4 11/10654 4.124092e-03 0.0135505893 2.480657e-03
## R-HSA-195258 2/4 327/10654 5.408852e-03 0.0155504494 2.846764e-03
## R-HSA-164938 1/4 20/10654 7.488851e-03 0.0191381744 3.503556e-03
## R-HSA-983170 1/4 25/10654 9.354473e-03 0.0197626051 3.617868e-03
## R-HSA-164952 1/4 27/10654 1.009998e-02 0.0197626051 3.617868e-03
## R-HSA-194315 2/4 455/10654 1.031092e-02 0.0197626051 3.617868e-03
## R-HSA-2424491 1/4 30/10654 1.121746e-02 0.0198462814 3.633187e-03
## R-HSA-2172127 1/4 43/10654 1.604894e-02 0.0263661192 4.826750e-03
## R-HSA-1236974 1/4 83/10654 3.080403e-02 0.0472328423 8.646745e-03
## R-HSA-877300 1/4 92/10654 3.410092e-02 0.0490200740 8.973927e-03
## R-HSA-1236975 1/4 99/10654 3.665935e-02 0.0495979451 9.079715e-03
## geneID Count
## R-HSA-6803157 4069/6279/6280 3
## R-HSA-6798695 567/4069/6279/6280 4
## R-HSA-5686938 6279/6280 2
## R-HSA-5668599 6279/6280 2
## R-HSA-977225 567/4069 2
## R-HSA-168898 6279/6280 2
## R-HSA-1236977 567 1
## R-HSA-195258 6279/6280 2
## R-HSA-164938 567 1
## R-HSA-983170 567 1
## R-HSA-164952 567 1
## R-HSA-194315 6279/6280 2
## R-HSA-2424491 567 1
## R-HSA-2172127 567 1
## R-HSA-1236974 567 1
## R-HSA-877300 567 1
## R-HSA-1236975 567 1
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_16"
## [1] "A2M" "ACTB" "ACTG1" "AIF1" "B2M" "C1QA"
## [7] "CD74" "CST3" "CTSB" "CTSS" "GRN" "HLA-A"
## [13] "HLA-B" "HLA-C" "HLA-DPA1" "HLA-DPB1" "HLA-DQB1" "HLA-DRA"
## [19] "HLA-DRB1" "HLA-DRB5" "HLA-E" "LCP1" "LYZ" "PSAP"
## [25] "RNASE1" "SAMHD1" "SH3BGRL3" "TMSB10" "TMSB4X" "TYROBP"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID Description GeneRatio BgRatio
## GO:0042605 GO:0042605 peptide antigen binding 10/30 31/17697
## GO:0033218 GO:0033218 amide binding 14/30 356/17697
## GO:0042277 GO:0042277 peptide binding 13/30 295/17697
## GO:0003823 GO:0003823 antigen binding 10/30 160/17697
## GO:0032395 GO:0032395 MHC class II receptor activity 3/30 10/17697
## GO:0023026 GO:0023026 MHC class II protein complex binding 3/30 16/17697
## GO:0023023 GO:0023023 MHC protein complex binding 3/30 25/17697
## GO:0099186 GO:0099186 structural constituent of postsynapse 2/30 11/17697
## GO:0050998 GO:0050998 nitric-oxide synthase binding 2/30 14/17697
## GO:0001540 GO:0001540 amyloid-beta binding 3/30 78/17697
## GO:0098918 GO:0098918 structural constituent of synapse 2/30 17/17697
## GO:0001871 GO:0001871 pattern binding 2/30 25/17697
## GO:0030247 GO:0030247 polysaccharide binding 2/30 25/17697
## GO:0003785 GO:0003785 actin monomer binding 2/30 28/17697
## GO:0002020 GO:0002020 protease binding 3/30 128/17697
## GO:0043394 GO:0043394 proteoglycan binding 2/30 36/17697
## GO:0042287 GO:0042287 MHC protein binding 2/30 40/17697
## GO:0005518 GO:0005518 collagen binding 2/30 67/17697
## GO:0003779 GO:0003779 actin binding 4/30 431/17697
## pvalue p.adjust qvalue
## GO:0042605 1.574614e-21 1.432899e-19 7.955944e-20
## GO:0033218 1.498224e-16 6.816920e-15 3.784987e-15
## GO:0042277 5.477842e-16 1.661612e-14 9.225839e-15
## GO:0003823 7.066848e-14 1.607708e-12 8.926545e-13
## GO:0032395 5.233019e-07 9.524095e-06 5.288104e-06
## GO:0023026 2.425359e-06 3.678462e-05 2.042408e-05
## GO:0023023 9.859218e-06 1.281698e-04 7.116428e-05
## GO:0099186 1.513503e-04 1.721609e-03 9.558965e-04
## GO:0050998 2.496250e-04 2.523986e-03 1.401403e-03
## GO:0001540 3.069341e-04 2.793100e-03 1.550825e-03
## GO:0098918 3.718878e-04 3.076527e-03 1.708193e-03
## GO:0001871 8.134547e-04 5.694183e-03 3.161605e-03
## GO:0030247 8.134547e-04 5.694183e-03 3.161605e-03
## GO:0003785 1.021720e-03 6.641177e-03 3.687409e-03
## GO:0002020 1.300906e-03 7.892164e-03 4.382000e-03
## GO:0043394 1.688586e-03 9.603833e-03 5.332377e-03
## GO:0042287 2.081851e-03 1.114402e-02 6.187544e-03
## GO:0005518 5.736207e-03 2.758055e-02 1.531367e-02
## GO:0003779 5.758577e-03 2.758055e-02 1.531367e-02
## geneID
## GO:0042605 3105/3106/3107/3113/3115/3119/3122/3123/3127/3133
## GO:0033218 712/972/1471/3105/3106/3107/3113/3115/3119/3122/3123/3127/3133/5660
## GO:0042277 712/972/1471/3105/3106/3107/3113/3115/3119/3122/3123/3127/3133
## GO:0003823 3105/3106/3107/3113/3115/3119/3122/3123/3127/3133
## GO:0032395 3113/3119/3122
## GO:0023026 972/3122/3123
## GO:0023023 972/3122/3123
## GO:0099186 60/71
## GO:0050998 60/972
## GO:0001540 712/972/1471
## GO:0098918 60/71
## GO:0001871 3122/3123
## GO:0030247 3122/3123
## GO:0003785 9168/7114
## GO:0002020 2/1471/5660
## GO:0043394 1508/1520
## GO:0042287 972/3133
## GO:0005518 1508/1520
## GO:0003779 199/3936/9168/7114
## Count
## GO:0042605 10
## GO:0033218 14
## GO:0042277 13
## GO:0003823 10
## GO:0032395 3
## GO:0023026 3
## GO:0023023 3
## GO:0099186 2
## GO:0050998 2
## GO:0001540 3
## GO:0098918 2
## GO:0001871 2
## GO:0030247 2
## GO:0003785 2
## GO:0002020 3
## GO:0043394 2
## GO:0042287 2
## GO:0005518 2
## GO:0003779 4
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-877300 R-HSA-877300
## R-HSA-913531 R-HSA-913531
## R-HSA-1236977 R-HSA-1236977
## R-HSA-202430 R-HSA-202430
## R-HSA-202427 R-HSA-202427
## R-HSA-2132295 R-HSA-2132295
## R-HSA-389948 R-HSA-389948
## R-HSA-202433 R-HSA-202433
## R-HSA-983170 R-HSA-983170
## R-HSA-388841 R-HSA-388841
## R-HSA-202424 R-HSA-202424
## R-HSA-6798695 R-HSA-6798695
## R-HSA-1236975 R-HSA-1236975
## R-HSA-202403 R-HSA-202403
## R-HSA-909733 R-HSA-909733
## R-HSA-198933 R-HSA-198933
## R-HSA-1236974 R-HSA-1236974
## R-HSA-2424491 R-HSA-2424491
## R-HSA-2172127 R-HSA-2172127
## R-HSA-983169 R-HSA-983169
## R-HSA-196025 R-HSA-196025
## R-HSA-190873 R-HSA-190873
## R-HSA-1679131 R-HSA-1679131
## R-HSA-446353 R-HSA-446353
## R-HSA-977225 R-HSA-977225
## R-HSA-445095 R-HSA-445095
## R-HSA-5626467 R-HSA-5626467
## R-HSA-418990 R-HSA-418990
## R-HSA-5663213 R-HSA-5663213
## R-HSA-6802948 R-HSA-6802948
## R-HSA-114608 R-HSA-114608
## R-HSA-1500931 R-HSA-1500931
## R-HSA-5674135 R-HSA-5674135
## R-HSA-76005 R-HSA-76005
## R-HSA-3928662 R-HSA-3928662
## R-HSA-1474228 R-HSA-1474228
## R-HSA-190828 R-HSA-190828
## R-HSA-437239 R-HSA-437239
## R-HSA-6802946 R-HSA-6802946
## R-HSA-6802949 R-HSA-6802949
## R-HSA-6802955 R-HSA-6802955
## R-HSA-9649948 R-HSA-9649948
## R-HSA-157858 R-HSA-157858
## R-HSA-3928665 R-HSA-3928665
## R-HSA-2022090 R-HSA-2022090
## R-HSA-2029482 R-HSA-2029482
## R-HSA-421270 R-HSA-421270
## R-HSA-6802952 R-HSA-6802952
## R-HSA-6802957 R-HSA-6802957
## R-HSA-1445148 R-HSA-1445148
## R-HSA-2029480 R-HSA-2029480
## Description
## R-HSA-877300 Interferon gamma signaling
## R-HSA-913531 Interferon Signaling
## R-HSA-1236977 Endosomal/Vacuolar pathway
## R-HSA-202430 Translocation of ZAP-70 to Immunological synapse
## R-HSA-202427 Phosphorylation of CD3 and TCR zeta chains
## R-HSA-2132295 MHC class II antigen presentation
## R-HSA-389948 PD-1 signaling
## R-HSA-202433 Generation of second messenger molecules
## R-HSA-983170 Antigen Presentation: Folding, assembly and peptide loading of class I MHC
## R-HSA-388841 Costimulation by the CD28 family
## R-HSA-202424 Downstream TCR signaling
## R-HSA-6798695 Neutrophil degranulation
## R-HSA-1236975 Antigen processing-Cross presentation
## R-HSA-202403 TCR signaling
## R-HSA-909733 Interferon alpha/beta signaling
## R-HSA-198933 Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell
## R-HSA-1236974 ER-Phagosome pathway
## R-HSA-2424491 DAP12 signaling
## R-HSA-2172127 DAP12 interactions
## R-HSA-983169 Class I MHC mediated antigen processing & presentation
## R-HSA-196025 Formation of annular gap junctions
## R-HSA-190873 Gap junction degradation
## R-HSA-1679131 Trafficking and processing of endosomal TLR
## R-HSA-446353 Cell-extracellular matrix interactions
## R-HSA-977225 Amyloid fiber formation
## R-HSA-445095 Interaction between L1 and Ankyrins
## R-HSA-5626467 RHO GTPases activate IQGAPs
## R-HSA-418990 Adherens junctions interactions
## R-HSA-5663213 RHO GTPases Activate WASPs and WAVEs
## R-HSA-6802948 Signaling by high-kinase activity BRAF mutants
## R-HSA-114608 Platelet degranulation
## R-HSA-1500931 Cell-Cell communication
## R-HSA-5674135 MAP2K and MAPK activation
## R-HSA-76005 Response to elevated platelet cytosolic Ca2+
## R-HSA-3928662 EPHB-mediated forward signaling
## R-HSA-1474228 Degradation of the extracellular matrix
## R-HSA-190828 Gap junction trafficking
## R-HSA-437239 Recycling pathway of L1
## R-HSA-6802946 Signaling by moderate kinase activity BRAF mutants
## R-HSA-6802949 Signaling by RAS mutants
## R-HSA-6802955 Paradoxical activation of RAF signaling by kinase inactive BRAF
## R-HSA-9649948 Signaling downstream of RAS mutants
## R-HSA-157858 Gap junction trafficking and regulation
## R-HSA-3928665 EPH-ephrin mediated repulsion of cells
## R-HSA-2022090 Assembly of collagen fibrils and other multimeric structures
## R-HSA-2029482 Regulation of actin dynamics for phagocytic cup formation
## R-HSA-421270 Cell-cell junction organization
## R-HSA-6802952 Signaling by BRAF and RAF fusions
## R-HSA-6802957 Oncogenic MAPK signaling
## R-HSA-1445148 Translocation of SLC2A4 (GLUT4) to the plasma membrane
## R-HSA-2029480 Fcgamma receptor (FCGR) dependent phagocytosis
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-877300 11/27 92/10654 1.252282e-16 1.590398e-14 8.040970e-15
## R-HSA-913531 12/27 199/10654 1.759968e-14 1.117580e-12 5.650425e-13
## R-HSA-1236977 6/27 11/10654 6.685625e-14 2.830248e-12 1.430958e-12
## R-HSA-202430 6/27 19/10654 3.873480e-12 1.229830e-10 6.217954e-11
## R-HSA-202427 6/27 22/10654 1.059810e-11 2.257797e-10 1.141530e-10
## R-HSA-2132295 9/27 123/10654 1.066676e-11 2.257797e-10 1.141530e-10
## R-HSA-389948 6/27 23/10654 1.431436e-11 2.597033e-10 1.313046e-10
## R-HSA-202433 6/27 33/10654 1.544165e-10 2.451361e-09 1.239395e-09
## R-HSA-983170 5/27 25/10654 3.626013e-09 5.116707e-08 2.586980e-08
## R-HSA-388841 6/27 70/10654 1.716861e-08 2.180413e-07 1.102405e-07
## R-HSA-202424 6/27 98/10654 1.314324e-07 1.364512e-06 6.898899e-07
## R-HSA-6798695 10/27 480/10654 1.325804e-07 1.364512e-06 6.898899e-07
## R-HSA-1236975 6/27 99/10654 1.396744e-07 1.364512e-06 6.898899e-07
## R-HSA-202403 6/27 119/10654 4.180917e-07 3.792689e-06 1.917563e-06
## R-HSA-909733 5/27 69/10654 7.109353e-07 6.019252e-06 3.043302e-06
## R-HSA-198933 6/27 132/10654 7.715844e-07 6.124451e-06 3.096490e-06
## R-HSA-1236974 5/27 83/10654 1.792813e-06 1.339337e-05 6.771616e-06
## R-HSA-2424491 3/27 30/10654 5.630719e-05 3.972785e-04 2.008619e-04
## R-HSA-2172127 3/27 43/10654 1.674369e-04 1.119183e-03 5.658531e-04
## R-HSA-983169 6/27 371/10654 2.722946e-04 1.729071e-03 8.742089e-04
## R-HSA-196025 2/27 11/10654 3.354268e-04 2.028534e-03 1.025616e-03
## R-HSA-190873 2/27 12/10654 4.018831e-04 2.319962e-03 1.172960e-03
## R-HSA-1679131 2/27 13/10654 4.742106e-04 2.618467e-03 1.323883e-03
## R-HSA-446353 2/27 18/10654 9.229398e-04 4.883890e-03 2.469269e-03
## R-HSA-977225 3/27 109/10654 2.548266e-03 1.294519e-02 6.545019e-03
## R-HSA-445095 2/27 31/10654 2.748647e-03 1.342608e-02 6.788156e-03
## R-HSA-5626467 2/27 32/10654 2.927319e-03 1.376924e-02 6.961656e-03
## R-HSA-418990 2/27 33/10654 3.111322e-03 1.411207e-02 7.134986e-03
## R-HSA-5663213 2/27 36/10654 3.695045e-03 1.564236e-02 7.908692e-03
## R-HSA-6802948 2/27 36/10654 3.695045e-03 1.564236e-02 7.908692e-03
## R-HSA-114608 3/27 129/10654 4.101985e-03 1.627975e-02 8.230957e-03
## R-HSA-1500931 3/27 129/10654 4.101985e-03 1.627975e-02 8.230957e-03
## R-HSA-5674135 2/27 40/10654 4.546381e-03 1.704484e-02 8.617783e-03
## R-HSA-76005 3/27 134/10654 4.563187e-03 1.704484e-02 8.617783e-03
## R-HSA-3928662 2/27 42/10654 5.002891e-03 1.815335e-02 9.178235e-03
## R-HSA-1474228 3/27 140/10654 5.156767e-03 1.819193e-02 9.197743e-03
## R-HSA-190828 2/27 47/10654 6.232490e-03 1.884586e-02 9.528368e-03
## R-HSA-437239 2/27 47/10654 6.232490e-03 1.884586e-02 9.528368e-03
## R-HSA-6802946 2/27 47/10654 6.232490e-03 1.884586e-02 9.528368e-03
## R-HSA-6802949 2/27 47/10654 6.232490e-03 1.884586e-02 9.528368e-03
## R-HSA-6802955 2/27 47/10654 6.232490e-03 1.884586e-02 9.528368e-03
## R-HSA-9649948 2/27 47/10654 6.232490e-03 1.884586e-02 9.528368e-03
## R-HSA-157858 2/27 49/10654 6.759134e-03 1.996302e-02 1.009320e-02
## R-HSA-3928665 2/27 51/10654 7.305368e-03 2.108595e-02 1.066094e-02
## R-HSA-2022090 2/27 61/10654 1.032356e-02 2.850199e-02 1.441046e-02
## R-HSA-2029482 2/27 61/10654 1.032356e-02 2.850199e-02 1.441046e-02
## R-HSA-421270 2/27 64/10654 1.131995e-02 3.058794e-02 1.546510e-02
## R-HSA-6802952 2/27 67/10654 1.235717e-02 3.269502e-02 1.653043e-02
## R-HSA-6802957 2/27 71/10654 1.380257e-02 3.577400e-02 1.808715e-02
## R-HSA-1445148 2/27 72/10654 1.417491e-02 3.600428e-02 1.820357e-02
## R-HSA-2029480 2/27 86/10654 1.983449e-02 4.939176e-02 2.497221e-02
## geneID Count
## R-HSA-877300 567/3105/3106/3107/3113/3115/3119/3122/3123/3127/3133 11
## R-HSA-913531 567/3105/3106/3107/3113/3115/3119/3122/3123/3127/3133/25939 12
## R-HSA-1236977 567/1520/3105/3106/3107/3133 6
## R-HSA-202430 3113/3115/3119/3122/3123/3127 6
## R-HSA-202427 3113/3115/3119/3122/3123/3127 6
## R-HSA-2132295 972/1508/1520/3113/3115/3119/3122/3123/3127 9
## R-HSA-389948 3113/3115/3119/3122/3123/3127 6
## R-HSA-202433 3113/3115/3119/3122/3123/3127 6
## R-HSA-983170 567/3105/3106/3107/3133 5
## R-HSA-388841 3113/3115/3119/3122/3123/3127 6
## R-HSA-202424 3113/3115/3119/3122/3123/3127 6
## R-HSA-6798695 567/1471/1508/1520/2896/3106/3107/4069/5660/7305 10
## R-HSA-1236975 567/1520/3105/3106/3107/3133 6
## R-HSA-202403 3113/3115/3119/3122/3123/3127 6
## R-HSA-909733 3105/3106/3107/3133/25939 5
## R-HSA-198933 567/3105/3106/3107/3133/7305 6
## R-HSA-1236974 567/3105/3106/3107/3133 5
## R-HSA-2424491 567/3133/7305 3
## R-HSA-2172127 567/3133/7305 3
## R-HSA-983169 567/1520/3105/3106/3107/3133 6
## R-HSA-196025 60/71 2
## R-HSA-190873 60/71 2
## R-HSA-1679131 1508/1520 2
## R-HSA-446353 60/71 2
## R-HSA-977225 567/1471/4069 3
## R-HSA-445095 60/71 2
## R-HSA-5626467 60/71 2
## R-HSA-418990 60/71 2
## R-HSA-5663213 60/71 2
## R-HSA-6802948 60/71 2
## R-HSA-114608 2/5660/7114 3
## R-HSA-1500931 60/71/7305 3
## R-HSA-5674135 60/71 2
## R-HSA-76005 2/5660/7114 3
## R-HSA-3928662 60/71 2
## R-HSA-1474228 2/1508/1520 3
## R-HSA-190828 60/71 2
## R-HSA-437239 60/71 2
## R-HSA-6802946 60/71 2
## R-HSA-6802949 60/71 2
## R-HSA-6802955 60/71 2
## R-HSA-9649948 60/71 2
## R-HSA-157858 60/71 2
## R-HSA-3928665 60/71 2
## R-HSA-2022090 1508/1520 2
## R-HSA-2029482 60/71 2
## R-HSA-421270 60/71 2
## R-HSA-6802952 60/71 2
## R-HSA-6802957 60/71 2
## R-HSA-1445148 60/71 2
## R-HSA-2029480 60/71 2
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## Warning in clusterProfiler::bitr(d$gene_name, fromType = "SYMBOL", toType =
## "ENTREZID", : 2.38% of input gene IDs are fail to map...## [1] "cluster_17"
## [1] "A2M" "ACTA2" "ACTG1" "ACTN4" "APOD" "B2M"
## [7] "BGN" "C12orf57" "CALD1" "CFH" "CLDN5" "CRIP2"
## [13] "CXCL12" "DCN" "EPAS1" "FLNA" "GJA4" "GSN"
## [19] "H3F3B" "HLA-A" "HLA-B" "HLA-C" "HLA-E" "ID1"
## [25] "IFI27" "IFITM3" "IGFBP3" "IGFBP7" "KCTD12" "LBH"
## [31] "MGP" "NOTCH3" "NR2F2" "RERGL" "ROCK1" "SEMA3G"
## [37] "SPARCL1" "TAGLN" "TIMP3" "TM4SF1" "TMSB10" "TSC22D1"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID
## GO:0042605 GO:0042605
## GO:0050840 GO:0050840
## GO:0003779 GO:0003779
## GO:0003823 GO:0003823
## GO:0005201 GO:0005201
## GO:0051015 GO:0051015
## GO:0030021 GO:0030021
## GO:0005520 GO:0005520
## GO:0047485 GO:0047485
## GO:0019838 GO:0019838
## GO:0042277 GO:0042277
## Description
## GO:0042605 peptide antigen binding
## GO:0050840 extracellular matrix binding
## GO:0003779 actin binding
## GO:0003823 antigen binding
## GO:0005201 extracellular matrix structural constituent
## GO:0051015 actin filament binding
## GO:0030021 extracellular matrix structural constituent conferring compression resistance
## GO:0005520 insulin-like growth factor binding
## GO:0047485 protein N-terminus binding
## GO:0019838 growth factor binding
## GO:0042277 peptide binding
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0042605 4/41 31/17697 7.454880e-07 8.573112e-05 5.963904e-05
## GO:0050840 3/41 57/17697 3.096109e-04 1.238829e-02 8.617940e-03
## GO:0003779 6/41 431/17697 4.400233e-04 1.238829e-02 8.617940e-03
## GO:0003823 4/41 160/17697 5.022056e-04 1.238829e-02 8.617940e-03
## GO:0005201 4/41 163/17697 5.386213e-04 1.238829e-02 8.617940e-03
## GO:0051015 4/41 198/17697 1.113585e-03 1.929862e-02 1.342513e-02
## GO:0030021 2/41 22/17697 1.174698e-03 1.929862e-02 1.342513e-02
## GO:0005520 2/41 28/17697 1.905403e-03 2.611337e-02 1.816582e-02
## GO:0047485 3/41 109/17697 2.043655e-03 2.611337e-02 1.816582e-02
## GO:0019838 3/41 137/17697 3.902284e-03 4.487626e-02 3.121827e-02
## GO:0042277 4/41 295/17697 4.716882e-03 4.931285e-02 3.430459e-02
## geneID Count
## GO:0042605 3105/3106/3107/3133 4
## GO:0050840 633/1634/8404 3
## GO:0003779 81/800/2316/2934/6876/9168 6
## GO:0003823 3105/3106/3107/3133 4
## GO:0005201 633/1634/3490/4256 4
## GO:0051015 81/2316/2934/6876 4
## GO:0030021 633/1634 2
## GO:0005520 3486/3490 2
## GO:0047485 81/1634/3397 3
## GO:0019838 2/3486/3490 3
## GO:0042277 3105/3106/3107/3133 4
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-1236977 R-HSA-1236977
## R-HSA-983170 R-HSA-983170
## R-HSA-909733 R-HSA-909733
## R-HSA-913531 R-HSA-913531
## R-HSA-1236974 R-HSA-1236974
## R-HSA-877300 R-HSA-877300
## R-HSA-1236975 R-HSA-1236975
## R-HSA-198933 R-HSA-198933
## R-HSA-2022923 R-HSA-2022923
## R-HSA-114608 R-HSA-114608
## R-HSA-1500931 R-HSA-1500931
## R-HSA-76005 R-HSA-76005
## R-HSA-2024101 R-HSA-2024101
## R-HSA-446353 R-HSA-446353
## R-HSA-2022870 R-HSA-2022870
## R-HSA-3560783 R-HSA-3560783
## R-HSA-3560801 R-HSA-3560801
## R-HSA-4420332 R-HSA-4420332
## R-HSA-446728 R-HSA-446728
## R-HSA-1971475 R-HSA-1971475
## R-HSA-2424491 R-HSA-2424491
## R-HSA-8957275 R-HSA-8957275
## R-HSA-983169 R-HSA-983169
## R-HSA-111465 R-HSA-111465
## R-HSA-381426 R-HSA-381426
## R-HSA-445355 R-HSA-445355
## R-HSA-3560782 R-HSA-3560782
## R-HSA-76002 R-HSA-76002
## R-HSA-3928662 R-HSA-3928662
## R-HSA-2172127 R-HSA-2172127
## R-HSA-3781865 R-HSA-3781865
## R-HSA-190828 R-HSA-190828
## R-HSA-157858 R-HSA-157858
## R-HSA-1793185 R-HSA-1793185
## R-HSA-75153 R-HSA-75153
## R-HSA-6798695 R-HSA-6798695
## R-HSA-1638091 R-HSA-1638091
## Description
## R-HSA-1236977 Endosomal/Vacuolar pathway
## R-HSA-983170 Antigen Presentation: Folding, assembly and peptide loading of class I MHC
## R-HSA-909733 Interferon alpha/beta signaling
## R-HSA-913531 Interferon Signaling
## R-HSA-1236974 ER-Phagosome pathway
## R-HSA-877300 Interferon gamma signaling
## R-HSA-1236975 Antigen processing-Cross presentation
## R-HSA-198933 Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell
## R-HSA-2022923 Dermatan sulfate biosynthesis
## R-HSA-114608 Platelet degranulation
## R-HSA-1500931 Cell-Cell communication
## R-HSA-76005 Response to elevated platelet cytosolic Ca2+
## R-HSA-2024101 CS/DS degradation
## R-HSA-446353 Cell-extracellular matrix interactions
## R-HSA-2022870 Chondroitin sulfate biosynthesis
## R-HSA-3560783 Defective B4GALT7 causes EDS, progeroid type
## R-HSA-3560801 Defective B3GAT3 causes JDSSDHD
## R-HSA-4420332 Defective B3GALT6 causes EDSP2 and SEMDJL1
## R-HSA-446728 Cell junction organization
## R-HSA-1971475 A tetrasaccharide linker sequence is required for GAG synthesis
## R-HSA-2424491 DAP12 signaling
## R-HSA-8957275 Post-translational protein phosphorylation
## R-HSA-983169 Class I MHC mediated antigen processing & presentation
## R-HSA-111465 Apoptotic cleavage of cellular proteins
## R-HSA-381426 Regulation of Insulin-like Growth Factor (IGF) transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs)
## R-HSA-445355 Smooth Muscle Contraction
## R-HSA-3560782 Diseases associated with glycosaminoglycan metabolism
## R-HSA-76002 Platelet activation, signaling and aggregation
## R-HSA-3928662 EPHB-mediated forward signaling
## R-HSA-2172127 DAP12 interactions
## R-HSA-3781865 Diseases of glycosylation
## R-HSA-190828 Gap junction trafficking
## R-HSA-157858 Gap junction trafficking and regulation
## R-HSA-1793185 Chondroitin sulfate/dermatan sulfate metabolism
## R-HSA-75153 Apoptotic execution phase
## R-HSA-6798695 Neutrophil degranulation
## R-HSA-1638091 Heparan sulfate/heparin (HS-GAG) metabolism
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-1236977 5/30 11/10654 5.693738e-11 8.540607e-09 5.813606e-09
## R-HSA-983170 5/30 25/10654 6.370670e-09 4.778002e-07 3.252395e-07
## R-HSA-909733 6/30 69/10654 3.106340e-08 1.553170e-06 1.057245e-06
## R-HSA-913531 8/30 199/10654 5.296007e-08 1.986003e-06 1.351876e-06
## R-HSA-1236974 5/30 83/10654 3.107236e-06 9.321709e-05 6.345304e-05
## R-HSA-877300 5/30 92/10654 5.170778e-06 1.292695e-04 8.799395e-05
## R-HSA-1236975 5/30 99/10654 7.417776e-06 1.589523e-04 1.081991e-04
## R-HSA-198933 5/30 132/10654 3.006760e-05 5.637674e-04 3.837575e-04
## R-HSA-2022923 2/30 11/10654 4.149979e-04 6.006265e-03 4.088475e-03
## R-HSA-114608 4/30 129/10654 4.404594e-04 6.006265e-03 4.088475e-03
## R-HSA-1500931 4/30 129/10654 4.404594e-04 6.006265e-03 4.088475e-03
## R-HSA-76005 4/30 134/10654 5.087357e-04 6.359196e-03 4.328716e-03
## R-HSA-2024101 2/30 14/10654 6.830347e-04 7.881170e-03 5.364726e-03
## R-HSA-446353 2/30 18/10654 1.140387e-03 1.176016e-02 8.005163e-03
## R-HSA-2022870 2/30 20/10654 1.411220e-03 1.176016e-02 8.005163e-03
## R-HSA-3560783 2/30 20/10654 1.411220e-03 1.176016e-02 8.005163e-03
## R-HSA-3560801 2/30 20/10654 1.411220e-03 1.176016e-02 8.005163e-03
## R-HSA-4420332 2/30 20/10654 1.411220e-03 1.176016e-02 8.005163e-03
## R-HSA-446728 3/30 91/10654 2.071222e-03 1.635175e-02 1.113067e-02
## R-HSA-1971475 2/30 26/10654 2.388736e-03 1.791552e-02 1.219512e-02
## R-HSA-2424491 2/30 30/10654 3.174964e-03 2.258068e-02 1.537071e-02
## R-HSA-8957275 3/30 108/10654 3.370481e-03 2.258068e-02 1.537071e-02
## R-HSA-983169 5/30 371/10654 3.462371e-03 2.258068e-02 1.537071e-02
## R-HSA-111465 2/30 38/10654 5.059878e-03 3.047945e-02 2.074741e-02
## R-HSA-381426 3/30 125/10654 5.079908e-03 3.047945e-02 2.074741e-02
## R-HSA-445355 2/30 40/10654 5.594539e-03 3.172147e-02 2.159286e-02
## R-HSA-3560782 2/30 41/10654 5.871191e-03 3.172147e-02 2.159286e-02
## R-HSA-76002 4/30 262/10654 5.921342e-03 3.172147e-02 2.159286e-02
## R-HSA-3928662 2/30 42/10654 6.154012e-03 3.183110e-02 2.166748e-02
## R-HSA-2172127 2/30 43/10654 6.442966e-03 3.221483e-02 2.192869e-02
## R-HSA-3781865 3/30 143/10654 7.373919e-03 3.568025e-02 2.428761e-02
## R-HSA-190828 2/30 47/10654 7.659432e-03 3.590359e-02 2.443963e-02
## R-HSA-157858 2/30 49/10654 8.303578e-03 3.774354e-02 2.569209e-02
## R-HSA-1793185 2/30 50/10654 8.634511e-03 3.809343e-02 2.593027e-02
## R-HSA-75153 2/30 52/10654 9.313931e-03 3.991685e-02 2.717147e-02
## R-HSA-6798695 5/30 480/10654 1.018677e-02 4.206667e-02 2.863485e-02
## R-HSA-1638091 2/30 55/10654 1.037644e-02 4.206667e-02 2.863485e-02
## geneID Count
## R-HSA-1236977 567/3105/3106/3107/3133 5
## R-HSA-983170 567/3105/3106/3107/3133 5
## R-HSA-909733 3105/3106/3107/3133/3429/10410 6
## R-HSA-913531 567/2316/3105/3106/3107/3133/3429/10410 8
## R-HSA-1236974 567/3105/3106/3107/3133 5
## R-HSA-877300 567/3105/3106/3107/3133 5
## R-HSA-1236975 567/3105/3106/3107/3133 5
## R-HSA-198933 567/3105/3106/3107/3133 5
## R-HSA-2022923 633/1634 2
## R-HSA-114608 2/81/2316/7078 4
## R-HSA-1500931 71/81/7122/2316 4
## R-HSA-76005 2/81/2316/7078 4
## R-HSA-2024101 633/1634 2
## R-HSA-446353 71/2316 2
## R-HSA-2022870 633/1634 2
## R-HSA-3560783 633/1634 2
## R-HSA-3560801 633/1634 2
## R-HSA-4420332 633/1634 2
## R-HSA-446728 71/7122/2316 3
## R-HSA-1971475 633/1634 2
## R-HSA-2424491 567/3133 2
## R-HSA-8957275 3486/3490/8404 3
## R-HSA-983169 567/3105/3106/3107/3133 5
## R-HSA-111465 2934/6093 2
## R-HSA-381426 3486/3490/8404 3
## R-HSA-445355 59/800 2
## R-HSA-3560782 633/1634 2
## R-HSA-76002 2/81/2316/7078 4
## R-HSA-3928662 71/6093 2
## R-HSA-2172127 567/3133 2
## R-HSA-3781865 633/1634/4854 3
## R-HSA-190828 71/2701 2
## R-HSA-157858 71/2701 2
## R-HSA-1793185 633/1634 2
## R-HSA-75153 2934/6093 2
## R-HSA-6798695 567/2934/3106/3107/6093 5
## R-HSA-1638091 633/1634 2
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_18"
## [1] "IGHG1" "IGHG3" "IGHG4" "IGKC" "JCHAIN" "SCD"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID Description GeneRatio BgRatio
## GO:0034987 GO:0034987 immunoglobulin receptor binding 5/6 76/17697
## GO:0003823 GO:0003823 antigen binding 5/6 160/17697
## GO:0042834 GO:0042834 peptidoglycan binding 1/6 17/17697
## GO:0019865 GO:0019865 immunoglobulin binding 1/6 24/17697
## GO:0031210 GO:0031210 phosphatidylcholine binding 1/6 27/17697
## GO:0050997 GO:0050997 quaternary ammonium group binding 1/6 27/17697
## pvalue p.adjust qvalue geneID
## GO:0034987 7.641973e-12 1.069876e-10 4.826510e-11 3500/3502/3503/3514/3512
## GO:0003823 3.379983e-10 2.365988e-09 1.067363e-09 3500/3502/3503/3514/3512
## GO:0042834 5.750675e-03 2.128124e-02 9.600561e-03 3512
## GO:0019865 8.110577e-03 2.128124e-02 9.600561e-03 3512
## GO:0031210 9.120533e-03 2.128124e-02 9.600561e-03 3512
## GO:0050997 9.120533e-03 2.128124e-02 9.600561e-03 3512
## Count
## GO:0034987 5
## GO:0003823 5
## GO:0042834 1
## GO:0019865 1
## GO:0031210 1
## GO:0050997 1
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-2168880 R-HSA-2168880
## R-HSA-75105 R-HSA-75105
## R-HSA-2173782 R-HSA-2173782
## R-HSA-2426168 R-HSA-2426168
## R-HSA-9024446 R-HSA-9024446
## R-HSA-1655829 R-HSA-1655829
## R-HSA-202733 R-HSA-202733
## R-HSA-8957322 R-HSA-8957322
## R-HSA-8978868 R-HSA-8978868
## Description GeneRatio
## R-HSA-2168880 Scavenging of heme from plasma 1/2
## R-HSA-75105 Fatty acyl-CoA biosynthesis 1/2
## R-HSA-2173782 Binding and Uptake of Ligands by Scavenger Receptors 1/2
## R-HSA-2426168 Activation of gene expression by SREBF (SREBP) 1/2
## R-HSA-9024446 NR1H2 and NR1H3-mediated signaling 1/2
## R-HSA-1655829 Regulation of cholesterol biosynthesis by SREBP (SREBF) 1/2
## R-HSA-202733 Cell surface interactions at the vascular wall 1/2
## R-HSA-8957322 Metabolism of steroids 1/2
## R-HSA-8978868 Fatty acid metabolism 1/2
## BgRatio pvalue p.adjust qvalue geneID Count
## R-HSA-2168880 13/10654 0.002439023 0.01716432 0.001806771 3512 1
## R-HSA-75105 37/10654 0.006934012 0.01716432 0.001806771 6319 1
## R-HSA-2173782 42/10654 0.007869190 0.01716432 0.001806771 3512 1
## R-HSA-2426168 42/10654 0.007869190 0.01716432 0.001806771 6319 1
## R-HSA-9024446 47/10654 0.008803928 0.01716432 0.001806771 6319 1
## R-HSA-1655829 55/10654 0.010298593 0.01716432 0.001806771 6319 1
## R-HSA-202733 137/10654 0.025553877 0.03518325 0.003703500 3512 1
## R-HSA-8957322 151/10654 0.028146596 0.03518325 0.003703500 6319 1
## R-HSA-8978868 177/10654 0.032952483 0.03661387 0.003854092 6319 1
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_19"
## [1] "ACTA2" "ACTG2" "AEBP1" "CALD1" "CNN1" "CSRP1" "DES"
## [8] "FLNA" "IGFBP5" "IGFBP7" "MGP" "MYH11" "MYLK" "SPARCL1"
## [15] "SYNPO2" "TAGLN" "TPM1" "TPM2"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID Description GeneRatio
## GO:0003779 GO:0003779 actin binding 9/18
## GO:0008307 GO:0008307 structural constituent of muscle 4/18
## GO:0051015 GO:0051015 actin filament binding 5/18
## GO:0005516 GO:0005516 calmodulin binding 5/18
## GO:0005520 GO:0005520 insulin-like growth factor binding 2/18
## GO:0005201 GO:0005201 extracellular matrix structural constituent 3/18
## GO:0042805 GO:0042805 actinin binding 2/18
## GO:0005518 GO:0005518 collagen binding 2/18
## GO:0005200 GO:0005200 structural constituent of cytoskeleton 2/18
## GO:0019838 GO:0019838 growth factor binding 2/18
## GO:0031994 GO:0031994 insulin-like growth factor I binding 1/18
## GO:0031005 GO:0031005 filamin binding 1/18
## GO:0005523 GO:0005523 tropomyosin binding 1/18
## GO:0017160 GO:0017160 Ral GTPase binding 1/18
## BgRatio pvalue p.adjust qvalue
## GO:0003779 431/17697 1.110705e-10 4.887102e-09 2.689076e-09
## GO:0008307 46/17697 1.190163e-07 2.618359e-06 1.440724e-06
## GO:0051015 198/17697 1.268697e-06 1.466433e-05 8.068891e-06
## GO:0005516 200/17697 1.333121e-06 1.466433e-05 8.068891e-06
## GO:0005520 28/17697 3.636073e-04 3.199744e-03 1.760625e-03
## GO:0005201 163/17697 5.654459e-04 4.146603e-03 2.281624e-03
## GO:0042805 46/17697 9.848561e-04 6.190524e-03 3.406269e-03
## GO:0005518 67/17697 2.077456e-03 1.142601e-02 6.287037e-03
## GO:0005200 102/17697 4.739084e-03 2.316886e-02 1.274841e-02
## GO:0019838 137/17697 8.392735e-03 3.692803e-02 2.031925e-02
## GO:0031994 12/17697 1.214116e-02 4.762883e-02 2.620725e-02
## GO:0031005 13/17697 1.314662e-02 4.762883e-02 2.620725e-02
## GO:0005523 15/17697 1.515463e-02 4.762883e-02 2.620725e-02
## GO:0017160 15/17697 1.515463e-02 4.762883e-02 2.620725e-02
## geneID Count
## GO:0003779 800/1264/2316/4629/4638/171024/6876/7168/7169 9
## GO:0008307 1465/4629/7168/7169 4
## GO:0051015 2316/4629/6876/7168/7169 5
## GO:0005516 165/800/1264/4629/4638 5
## GO:0005520 3488/3490 2
## GO:0005201 165/3490/4256 3
## GO:0042805 1465/171024 2
## GO:0005518 165/8404 2
## GO:0005200 1674/7168 2
## GO:0019838 3488/3490 2
## GO:0031994 3488 1
## GO:0031005 171024 1
## GO:0005523 800 1
## GO:0017160 2316 1
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-445355 R-HSA-445355
## R-HSA-397014 R-HSA-397014
## R-HSA-5627123 R-HSA-5627123
## R-HSA-390522 R-HSA-390522
## R-HSA-8957275 R-HSA-8957275
## R-HSA-381426 R-HSA-381426
## R-HSA-195258 R-HSA-195258
## Description
## R-HSA-445355 Smooth Muscle Contraction
## R-HSA-397014 Muscle contraction
## R-HSA-5627123 RHO GTPases activate PAKs
## R-HSA-390522 Striated Muscle Contraction
## R-HSA-8957275 Post-translational protein phosphorylation
## R-HSA-381426 Regulation of Insulin-like Growth Factor (IGF) transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs)
## R-HSA-195258 RHO GTPase Effectors
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-445355 7/13 40/10654 1.020156e-14 3.366516e-13 1.718158e-13
## R-HSA-397014 8/13 209/10654 2.270674e-11 3.746613e-10 1.912147e-10
## R-HSA-5627123 3/13 24/10654 2.830648e-06 3.113713e-05 1.589136e-05
## R-HSA-390522 3/13 36/10654 9.901474e-06 8.168716e-05 4.169041e-05
## R-HSA-8957275 3/13 108/10654 2.690889e-04 1.775987e-03 9.064048e-04
## R-HSA-381426 3/13 125/10654 4.138114e-04 2.275963e-03 1.161576e-03
## R-HSA-195258 3/13 327/10654 6.517102e-03 3.072348e-02 1.568024e-02
## geneID Count
## R-HSA-445355 59/72/800/4629/4638/7168/7169 7
## R-HSA-397014 59/72/800/1674/4629/4638/7168/7169 8
## R-HSA-5627123 2316/4629/4638 3
## R-HSA-390522 1674/7168/7169 3
## R-HSA-8957275 3488/3490/8404 3
## R-HSA-381426 3488/3490/8404 3
## R-HSA-195258 2316/4629/4638 3
## [1] "---------------------------------"
## [1] "=================================="## 'select()' returned 1:1 mapping between keys and columns## [1] "cluster_20"
## [1] "ACP5" "ACTR3" "AIF1" "ALCAM" "APOC1" "ARHGDIB"
## [7] "ARPC1B" "B2M" "C1orf162" "C1QA" "C1QB" "CALM3"
## [13] "CAPG" "CCDC88A" "CD163" "CD52" "CD68" "CD74"
## [19] "CHI3L1" "CSTB" "CTSB" "CTSD" "CTSS" "CYBA"
## [25] "EPB41L2" "FABP5" "FBP1" "FCER1G" "FTH1" "FTL"
## [31] "FYB1" "GLIPR1" "GPNMB" "GRN" "HCST" "HLA-B"
## [37] "HLA-DRA" "HLA-DRB1" "IGSF6" "IQGAP2" "ITGB2" "LAPTM5"
## [43] "LCP1" "LGMN" "LIPA" "LSP1" "LYZ" "MMP9"
## [49] "MS4A7" "MSR1" "NPC2" "PLA2G7" "PLD3" "PTPRC"
## [55] "SAMHD1" "SELENOP" "SH3BGRL3" "SPP1" "SRGN" "STMN1"
## [61] "TMSB4X" "TPM3" "TREM2" "TUBA1B" "TYROBP" "VIM"
## [1] "---------------------------------"
## [1] "GO Enrichment: "
## ID
## GO:0003779 GO:0003779
## GO:0051015 GO:0051015
## GO:0008199 GO:0008199
## GO:0043394 GO:0043394
## GO:0001540 GO:0001540
## GO:0042277 GO:0042277
## GO:0023026 GO:0023026
## GO:0033218 GO:0033218
## GO:0008198 GO:0008198
## GO:0023023 GO:0023023
## GO:0042605 GO:0042605
## GO:0005200 GO:0005200
## GO:0004322 GO:0004322
## GO:0016724 GO:0016724
## GO:0050998 GO:0050998
## GO:0005178 GO:0005178
## GO:0030169 GO:0030169
## GO:0005518 GO:0005518
## GO:0016722 GO:0016722
## GO:0038024 GO:0038024
## GO:0001871 GO:0001871
## GO:0030247 GO:0030247
## GO:0030507 GO:0030507
## GO:0043548 GO:0043548
## GO:0071813 GO:0071813
## GO:0071814 GO:0071814
## GO:0005504 GO:0005504
## GO:0004197 GO:0004197
## Description
## GO:0003779 actin binding
## GO:0051015 actin filament binding
## GO:0008199 ferric iron binding
## GO:0043394 proteoglycan binding
## GO:0001540 amyloid-beta binding
## GO:0042277 peptide binding
## GO:0023026 MHC class II protein complex binding
## GO:0033218 amide binding
## GO:0008198 ferrous iron binding
## GO:0023023 MHC protein complex binding
## GO:0042605 peptide antigen binding
## GO:0005200 structural constituent of cytoskeleton
## GO:0004322 ferroxidase activity
## GO:0016724 oxidoreductase activity, oxidizing metal ions, oxygen as acceptor
## GO:0050998 nitric-oxide synthase binding
## GO:0005178 integrin binding
## GO:0030169 low-density lipoprotein particle binding
## GO:0005518 collagen binding
## GO:0016722 oxidoreductase activity, oxidizing metal ions
## GO:0038024 cargo receptor activity
## GO:0001871 pattern binding
## GO:0030247 polysaccharide binding
## GO:0030507 spectrin binding
## GO:0043548 phosphatidylinositol 3-kinase binding
## GO:0071813 lipoprotein particle binding
## GO:0071814 protein-lipid complex binding
## GO:0005504 fatty acid binding
## GO:0004197 cysteine-type endopeptidase activity
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0003779 11/61 431/17697 2.200410e-07 4.048755e-05 2.918439e-05
## GO:0051015 7/61 198/17697 5.165028e-06 2.275463e-04 1.640208e-04
## GO:0008199 3/61 11/17697 6.304392e-06 2.275463e-04 1.640208e-04
## GO:0043394 4/61 36/17697 6.928387e-06 2.275463e-04 1.640208e-04
## GO:0001540 5/61 78/17697 7.165458e-06 2.275463e-04 1.640208e-04
## GO:0042277 8/61 295/17697 7.419989e-06 2.275463e-04 1.640208e-04
## GO:0023026 3/61 16/17697 2.113531e-05 5.555567e-04 4.004585e-04
## GO:0033218 8/61 356/17697 2.881579e-05 6.627631e-04 4.777354e-04
## GO:0008198 3/61 24/17697 7.490245e-05 1.531339e-03 1.103826e-03
## GO:0023023 3/61 25/17697 8.490769e-05 1.562302e-03 1.126144e-03
## GO:0042605 3/61 31/17697 1.635136e-04 2.735136e-03 1.971551e-03
## GO:0005200 4/61 102/17697 4.218511e-04 6.468383e-03 4.662565e-03
## GO:0004322 2/61 10/17697 5.166467e-04 6.790214e-03 4.894548e-03
## GO:0016724 2/61 10/17697 5.166467e-04 6.790214e-03 4.894548e-03
## GO:0050998 2/61 14/17697 1.035537e-03 1.270258e-02 9.156324e-03
## GO:0005178 4/61 132/17697 1.110722e-03 1.277330e-02 9.207302e-03
## GO:0030169 2/61 16/17697 1.359497e-03 1.471455e-02 1.060660e-02
## GO:0005518 3/61 67/17697 1.595638e-03 1.631097e-02 1.175734e-02
## GO:0016722 2/61 19/17697 1.924439e-03 1.863667e-02 1.343375e-02
## GO:0038024 3/61 85/17697 3.148539e-03 2.786458e-02 2.008546e-02
## GO:0001871 2/61 25/17697 3.331635e-03 2.786458e-02 2.008546e-02
## GO:0030247 2/61 25/17697 3.331635e-03 2.786458e-02 2.008546e-02
## GO:0030507 2/61 28/17697 4.170088e-03 3.336070e-02 2.404719e-02
## GO:0043548 2/61 30/17697 4.777734e-03 3.662930e-02 2.640327e-02
## GO:0071813 2/61 33/17697 5.760861e-03 4.076917e-02 2.938739e-02
## GO:0071814 2/61 33/17697 5.760861e-03 4.076917e-02 2.938739e-02
## GO:0005504 2/61 34/17697 6.107410e-03 4.162087e-02 3.000131e-02
## GO:0004197 3/61 116/17697 7.493020e-03 4.923985e-02 3.549325e-02
## geneID Count
## GO:0003779 10096/199/10095/822/55704/2037/10788/3936/4046/7114/7170 11
## GO:0051015 10096/199/10095/822/10788/3936/7170 7
## GO:0008199 54/2495/2512 3
## GO:0043394 1508/1520/10457/5788 4
## GO:0001540 712/972/3689/4481/54209 5
## GO:0042277 712/972/3106/3122/3123/3689/4481/54209 8
## GO:0023026 972/3122/3123 3
## GO:0033218 712/972/3106/3122/3123/3689/4481/54209 8
## GO:0008198 54/2495/2512 3
## GO:0023023 972/3122/3123 3
## GO:0042605 3106/3122/3123 3
## GO:0005200 10096/10095/10376/7431 4
## GO:0004322 2495/2512 2
## GO:0016724 2495/2512 2
## GO:0050998 808/972 2
## GO:0005178 10457/3689/3936/6696 4
## GO:0030169 4481/54209 2
## GO:0005518 1508/1520/4318 3
## GO:0016722 2495/2512 2
## GO:0038024 9332/3689/4481 3
## GO:0001871 3122/3123 2
## GO:0030247 3122/3123 2
## GO:0030507 2037/5788 2
## GO:0043548 808/10870 2
## GO:0071813 4481/54209 2
## GO:0071814 4481/54209 2
## GO:0005504 341/2171 2
## GO:0004197 1508/1520/5641 3
## [1] "---------------------------------"
## [1] "Reactome Enrichment: "
## ID
## R-HSA-6798695 R-HSA-6798695
## R-HSA-2132295 R-HSA-2132295
## R-HSA-1236977 R-HSA-1236977
## R-HSA-1679131 R-HSA-1679131
## R-HSA-198933 R-HSA-198933
## R-HSA-2173782 R-HSA-2173782
## R-HSA-3000480 R-HSA-3000480
## R-HSA-416700 R-HSA-416700
## R-HSA-202427 R-HSA-202427
## R-HSA-2424491 R-HSA-2424491
## R-HSA-5626467 R-HSA-5626467
## R-HSA-202433 R-HSA-202433
## R-HSA-8964043 R-HSA-8964043
## R-HSA-1474228 R-HSA-1474228
## R-HSA-877300 R-HSA-877300
## R-HSA-2172127 R-HSA-2172127
## R-HSA-1236975 R-HSA-1236975
## R-HSA-166786 R-HSA-166786
## R-HSA-391160 R-HSA-391160
## R-HSA-202403 R-HSA-202403
## R-HSA-913531 R-HSA-913531
## R-HSA-2022090 R-HSA-2022090
## R-HSA-202430 R-HSA-202430
## R-HSA-8964038 R-HSA-8964038
## R-HSA-1442490 R-HSA-1442490
## R-HSA-114608 R-HSA-114608
## R-HSA-373755 R-HSA-373755
## Description
## R-HSA-6798695 Neutrophil degranulation
## R-HSA-2132295 MHC class II antigen presentation
## R-HSA-1236977 Endosomal/Vacuolar pathway
## R-HSA-1679131 Trafficking and processing of endosomal TLR
## R-HSA-198933 Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell
## R-HSA-2173782 Binding and Uptake of Ligands by Scavenger Receptors
## R-HSA-3000480 Scavenging by Class A Receptors
## R-HSA-416700 Other semaphorin interactions
## R-HSA-202427 Phosphorylation of CD3 and TCR zeta chains
## R-HSA-2424491 DAP12 signaling
## R-HSA-5626467 RHO GTPases activate IQGAPs
## R-HSA-202433 Generation of second messenger molecules
## R-HSA-8964043 Plasma lipoprotein clearance
## R-HSA-1474228 Degradation of the extracellular matrix
## R-HSA-877300 Interferon gamma signaling
## R-HSA-2172127 DAP12 interactions
## R-HSA-1236975 Antigen processing-Cross presentation
## R-HSA-166786 Creation of C4 and C2 activators
## R-HSA-391160 Signal regulatory protein family interactions
## R-HSA-202403 TCR signaling
## R-HSA-913531 Interferon Signaling
## R-HSA-2022090 Assembly of collagen fibrils and other multimeric structures
## R-HSA-202430 Translocation of ZAP-70 to Immunological synapse
## R-HSA-8964038 LDL clearance
## R-HSA-1442490 Collagen degradation
## R-HSA-114608 Platelet degranulation
## R-HSA-373755 Semaphorin interactions
## GeneRatio BgRatio pvalue p.adjust qvalue
## R-HSA-6798695 22/57 480/10654 1.208785e-15 3.239544e-13 2.722948e-13
## R-HSA-2132295 8/57 123/10654 2.584295e-07 3.462956e-05 2.910733e-05
## R-HSA-1236977 3/57 11/10654 2.324159e-05 2.076249e-03 1.745158e-03
## R-HSA-1679131 3/57 13/10654 3.998010e-05 2.678667e-03 2.251511e-03
## R-HSA-198933 6/57 132/10654 6.977282e-05 3.163880e-03 2.659349e-03
## R-HSA-2173782 4/57 42/10654 7.083313e-05 3.163880e-03 2.659349e-03
## R-HSA-3000480 3/57 19/10654 1.324027e-04 4.435492e-03 3.728183e-03
## R-HSA-416700 3/57 19/10654 1.324027e-04 4.435492e-03 3.728183e-03
## R-HSA-202427 3/57 22/10654 2.080395e-04 6.194953e-03 5.207069e-03
## R-HSA-2424491 3/57 30/10654 5.320706e-04 1.425949e-02 1.198559e-02
## R-HSA-5626467 3/57 32/10654 6.451082e-04 1.457374e-02 1.224973e-02
## R-HSA-202433 3/57 33/10654 7.069351e-04 1.457374e-02 1.224973e-02
## R-HSA-8964043 3/57 33/10654 7.069351e-04 1.457374e-02 1.224973e-02
## R-HSA-1474228 5/57 140/10654 8.823772e-04 1.689122e-02 1.419765e-02
## R-HSA-877300 4/57 92/10654 1.450212e-03 2.578876e-02 2.167634e-02
## R-HSA-2172127 3/57 43/10654 1.539628e-03 2.578876e-02 2.167634e-02
## R-HSA-1236975 4/57 99/10654 1.900442e-03 2.995991e-02 2.518233e-02
## R-HSA-166786 2/57 14/10654 2.455757e-03 3.656349e-02 3.073286e-02
## R-HSA-391160 2/57 16/10654 3.216205e-03 4.536542e-02 3.813119e-02
## R-HSA-202403 4/57 119/10654 3.704526e-03 4.964065e-02 4.172467e-02
## R-HSA-913531 5/57 199/10654 4.120874e-03 4.978101e-02 4.184264e-02
## R-HSA-2022090 3/57 61/10654 4.195037e-03 4.978101e-02 4.184264e-02
## R-HSA-202430 2/57 19/10654 4.536184e-03 4.978101e-02 4.184264e-02
## R-HSA-8964038 2/57 19/10654 4.536184e-03 4.978101e-02 4.184264e-02
## R-HSA-1442490 3/57 64/10654 4.801827e-03 4.978101e-02 4.184264e-02
## R-HSA-114608 4/57 129/10654 4.937619e-03 4.978101e-02 4.184264e-02
## R-HSA-373755 3/57 65/10654 5.015251e-03 4.978101e-02 4.184264e-02
## geneID
## R-HSA-6798695 567/968/1116/1476/1508/1509/1520/1535/2171/2207/2495/2512/11010/2896/3106/10788/3689/4069/4318/10577/5788/7305
## R-HSA-2132295 972/1508/1509/1520/3122/3123/5641/10376
## R-HSA-1236977 567/1520/3106
## R-HSA-1679131 1508/1520/5641
## R-HSA-198933 567/10870/3106/3689/54209/7305
## R-HSA-2173782 9332/2495/2512/4481
## R-HSA-3000480 2495/2512/4481
## R-HSA-416700 5788/54209/7305
## R-HSA-202427 3122/3123/5788
## R-HSA-2424491 567/54209/7305
## R-HSA-5626467 808/10788/10376
## R-HSA-202433 2533/3122/3123
## R-HSA-8964043 341/3988/10577
## R-HSA-1474228 1508/1509/1520/4318/6696
## R-HSA-877300 567/3106/3122/3123
## R-HSA-2172127 567/54209/7305
## R-HSA-1236975 567/1520/1535/3106
## R-HSA-166786 712/713
## R-HSA-391160 2533/7305
## R-HSA-202403 2533/3122/3123/5788
## R-HSA-913531 567/3106/3122/3123/25939
## R-HSA-2022090 1508/1520/4318
## R-HSA-202430 3122/3123
## R-HSA-8964038 3988/10577
## R-HSA-1442490 1508/1509/4318
## R-HSA-114608 808/6414/5552/7114
## R-HSA-373755 5788/54209/7305
## Count
## R-HSA-6798695 22
## R-HSA-2132295 8
## R-HSA-1236977 3
## R-HSA-1679131 3
## R-HSA-198933 6
## R-HSA-2173782 4
## R-HSA-3000480 3
## R-HSA-416700 3
## R-HSA-202427 3
## R-HSA-2424491 3
## R-HSA-5626467 3
## R-HSA-202433 3
## R-HSA-8964043 3
## R-HSA-1474228 5
## R-HSA-877300 4
## R-HSA-2172127 3
## R-HSA-1236975 4
## R-HSA-166786 2
## R-HSA-391160 2
## R-HSA-202403 4
## R-HSA-913531 5
## R-HSA-2022090 3
## R-HSA-202430 2
## R-HSA-8964038 2
## R-HSA-1442490 3
## R-HSA-114608 4
## R-HSA-373755 3
## [1] "---------------------------------"
## [1] "=================================="fname <- paste0(ANALYSIS_ID, ".markers_pathwayenrichment.xlsx")
sheets <- list()
for (c_name in names(pw_results_go)) {
pw_res <- pw_results_go[[c_name]]@result
sheets[paste0("GO_",c_name)] <- list(pw_res)
}
for (c_name in names(pw_results_ra)) {
pw_res <- pw_results_ra[[c_name]]@result
sheets[paste0("RA_",c_name)] <- list(pw_res)
}
write_xlsx(
x = sheets,
path = file.path(DIR_RES, "tables", fname),
col_names = TRUE,
format_headers = TRUE
)Other
Correlation of marker genes with LEP expression
markers_C3 <- FindMarkers(se, ident.1 = "3", only.pos = T, logfc.threshold = 0.15)
markers_C4 <- FindMarkers(se, ident.1 = "4", only.pos = T, logfc.threshold = 0.15)
genes_corr <- c(rownames(markers_C3), rownames(markers_C4))
exp_mat <- as.matrix(se@assays$SCT@data[genes_corr, ])
exp_goi <- as.numeric(exp_mat["LEP",])
gene_corr <- apply(exp_mat, 1, function(x){cor(exp_goi, x)})
gene_df_out <- data.frame(gene = genes_corr,
corr = gene_corr,
cluster = c(rep("C3", length(rownames(markers_C3))), rep("C4", length(rownames(markers_C4))) )
)
gene_df_out <- gene_df_out[!rownames(gene_df_out) %in% "LEP", ]
p1 <- ggplot(gene_df_out, aes(x=cluster, y=corr)) +
geom_boxplot() +
theme_linedraw()
p2 <- ggplot(gene_df_out, aes(x=reorder(gene, corr), y=corr, fill=cluster)) +
geom_col() +
labs(x="") +
coord_flip() +
theme_linedraw()
p <- (p1+p2) + plot_annotation(title="Gene correlations with LEP");p
pdf(file = file.path(DIR_FIG, "LEP_gene_corr_C3-C4.pdf"), width = 10, height = 8, useDingbats = F);p;dev.off()## quartz_off_screen
## 2write.csv(gene_df_out, file.path(DIR_FIG, "LEP_gene_corr_C3-C4.csv"), row.names = F)Cell signature expression
Adapted cell signatures for ATM from Acosta et al. (2017) (DOI 10.1186/s13287-017-0701-4).
cellsign_list <- list(ATM_M1 = c("HLA-DRA", "HLA-DPA1", "HLA-DPB1", "HLA-DRB5", "FCER1A", "CD1C", "IL1R2", "HLA-DQA2", "HLA-DRB3"),
ATM_M2 = c("RNASE1", "SEPP1", "JUND", "DNAJB1", "LYVE1", "MAF", "MAFB", "F13A1", "RHOB", "TRA2B"),
ATM_Int = c("FTL", "FTH1", "CTSB", "CCL3", "PLIN2", "FABP5", "PSAP", "CCL4", "CTSD", "IL1RN"))
cell_spot_sign <- list()
for ( cell in names(cellsign_list)){
# print(cell)
cell_signature_genes <- cellsign_list[[cell]]
cell_signature_genes_intersect <- intersect(cell_signature_genes, rownames(se))
print(c(cell, paste(cell_signature_genes_intersect)))
cell_spot_sign[[cell]] <- colSums(GetAssayData(se, slot = "data", assay = "SCT")[cell_signature_genes_intersect, ])
print(summary(cell_spot_sign[[cell]]))
se <- AddMetaData(se, cell_spot_sign[[cell]], col.name = cell)
}## [1] "ATM_M1" "HLA-DRA" "HLA-DPA1" "HLA-DPB1" "HLA-DRB5" "FCER1A" "HLA-DQA2"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3344 0.6931 7.2724
## [1] "ATM_M2" "RNASE1" "JUND" "DNAJB1" "LYVE1" "MAF" "MAFB" "F13A1"
## [9] "RHOB" "TRA2B"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.6931 0.9936 1.3863 7.3369
## [1] "ATM_Int" "FTL" "FTH1" "CTSB" "PLIN2" "FABP5" "PSAP"
## [8] "CTSD"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 3.989 4.970 5.052 6.061 20.308cut_M1 <- median(se@meta.data[se@meta.data$ATM_M1>0, "ATM_M1"])
# print(cut_M1)
se@meta.data$ATM_M1_cat <- "low"
se@meta.data[se@meta.data$ATM_M1 > cut_M1, "ATM_M1_cat"] <- "high"
df_M1_prop <- se@meta.data
df_M1_prop <- df_M1_prop %>%
group_by(condition, subject_alias, ATM_M1_cat) %>%
dplyr::count()
df_M1_prop$pct <- 0
for(s in unique(df_M1_prop$subject_alias)) {
sum_spots <- sum(df_M1_prop[df_M1_prop$subject_alias %in% s, "n"])
df_M1_prop[df_M1_prop$subject_alias %in% s, "pct"] <- (round(df_M1_prop[df_M1_prop$subject_alias %in% s, "n"] / sum_spots, digits = 3))*100
}
df_M1_prop$cut <- cut_M1
df_M1_prop## # A tibble: 20 x 6
## # Groups: condition, subject_alias, ATM_M1_cat [20]
## condition subject_alias ATM_M1_cat n pct cut
## <chr> <chr> <chr> <int> <dbl> <dbl>
## 1 lean Le.1 high 88 4.2 0.693
## 2 lean Le.1 low 1993 95.8 0.693
## 3 lean Le.2 high 326 15 0.693
## 4 lean Le.2 low 1854 85 0.693
## 5 lean Le.3 high 445 11.3 0.693
## 6 lean Le.3 low 3504 88.7 0.693
## 7 obese Ob.1 high 287 8.2 0.693
## 8 obese Ob.1 low 3210 91.8 0.693
## 9 obese Ob.2 high 207 12.8 0.693
## 10 obese Ob.2 low 1406 87.2 0.693
## 11 obese Ob.3 high 300 6.6 0.693
## 12 obese Ob.3 low 4220 93.4 0.693
## 13 obese Ob.4 high 468 24 0.693
## 14 obese Ob.4 low 1482 76 0.693
## 15 obese Ob.5 high 593 26.8 0.693
## 16 obese Ob.5 low 1622 73.2 0.693
## 17 overweight Ov.1 high 409 21.9 0.693
## 18 overweight Ov.1 low 1458 78.1 0.693
## 19 overweight Ov.2 high 647 15.9 0.693
## 20 overweight Ov.2 low 3418 84.1 0.693Plots
QC
feat_plot <- c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.MTRNR", "percent.HB.genes", "percent.MALAT1", "percent.RP")
VlnPlot(se, group.by = "sample_id",
features = feat_plot,
ncol = length(feat_plot),
pt.size = 0)
p <- VlnPlot(se,
group.by = "subject_alias",
features = c("nFeature_RNA", "nCount_RNA"),
ncol = 2,
cols = c(rep(colors_multi[1], 3), rep(colors_multi[2], 5), rep(colors_multi[3], 2)),
pt.size = 0) &
scale_y_log10() &
theme(axis.title.x = element_blank()) &
geom_boxplot(width=0.2, outlier.size = .5);p
fname <- paste0(ANALYSIS_ID, ".qc_stats_violin")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 6, height = 4);p;dev.off()## quartz_off_screen
## 2d_plot <- data.frame(se[[]])
d_plot1 <- d_plot[, c("subject_alias", "nCount_RNA")]
colnames(d_plot1) <- c("subject_alias", "qc_value")
d_plot1$qc_stat <- "UMIs"
d_plot2 <- d_plot[, c("subject_alias", "nFeature_RNA")]
colnames(d_plot2) <- c("subject_alias", "qc_value")
d_plot2$qc_stat <- "Genes"
d_plot3 <- rbind(d_plot1, d_plot2)
p <- ggplot(d_plot3, aes(x=qc_stat, y=qc_value)) +
geom_violin(fill="grey80", color=NA) +
geom_boxplot(width=0.2, outlier.size = 0.2) +
scale_y_log10() +
annotation_logticks(sides = "l") +
labs(title="", x="", y="count per spot") +
theme_classic() +
theme(plot.title = element_text(hjust=0.5, face = "bold"), legend.position = "none", legend.title = element_blank());p
fname <- paste0(ANALYSIS_ID, ".qc_stats_violin2")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 3, height = 3);p;dev.off()## quartz_off_screen
## 2color_scale <- c("white", "orange", "red")
p1 <- ST.FeaturePlot(se, features = c("percent.mt"), dark.theme = F, cols = color_scale, max.cutoff = 40)
p2 <- ST.FeaturePlot(se, features = c("percent.HB.genes"), dark.theme = F, cols = color_scale, max.cutoff = 20)
p3 <- ST.FeaturePlot(se, features = c("percent.MTRNR"), dark.theme = F, cols = color_scale)
p4 <- ST.FeaturePlot(se, features = c("percent.MALAT1"), dark.theme = F, cols = color_scale)
p5 <- ST.FeaturePlot(se, features = c("percent.RP"), dark.theme = F, cols = color_scale)
p <- (p1+p2+p3)/(p4+p5+patchwork::plot_spacer());p
fname <- paste0(ANALYSIS_ID, ".qc_stats_spatial")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 32, height = 18);p;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 32*2.8, height = 18*2.8, res = 600); p; dev.off()## quartz_off_screen
## 2Dimensionality reduction
ICA
fname <- paste0(ANALYSIS_ID, ".dimred_ica_hm")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 14, height = 20)
p1 <- DimHeatmap(se, dims = 1:18, cells = 500, nfeatures = 16, balanced = TRUE, reduction = "ica", ncol = 3);p1## NULLp2 <- DimHeatmap(se, dims = 19:36, cells = 500, nfeatures = 16, balanced = TRUE, reduction = "ica", ncol = 3);p2## NULLp3 <- DimHeatmap(se, dims = 37:50, cells = 500, nfeatures = 16, balanced = TRUE, reduction = "ica", ncol = 3);p3## NULLdev.off()## quartz_off_screen
## 2Harmony
fname <- paste0(ANALYSIS_ID, ".dimred_harmony_hm")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 14, height = 20)
p1 <- DimHeatmap(se, dims = 1:18, cells = 500, nfeatures = 16, balanced = TRUE, reduction = "harmony", ncol = 3);p1## NULLp2 <- DimHeatmap(se, dims = 19:36, cells = 500, nfeatures = 16, balanced = TRUE, reduction = "harmony", ncol = 3);p2## NULLp3 <- DimHeatmap(se, dims = 37:50, cells = 500, nfeatures = 16, balanced = TRUE, reduction = "harmony", ncol = 3);p3## NULLdev.off()## quartz_off_screen
## 2UMAP
pt_size <- 0.25
p1 <- DimPlot(object = se, dims = c(1,2), reduction = "umap", group.by = "subject_alias", pt.size = pt_size, cols = colors_multi) + NoAxes() #+ DarkTheme()
p2 <- DimPlot(object = se, dims = c(1,2), reduction = "umap", group.by = "condition", pt.size = pt_size, cols = colors_multi) + NoAxes() #+ DarkTheme()
p3 <- FeaturePlot(object = se, dims = c(1,2), reduction = "umap", features = "nCount_RNA", pt.size = pt_size, cols = c("grey90", "red"), max.cutoff = 5000) + NoAxes()
p4 <- FeaturePlot(object = se, dims = c(1,2), reduction = "umap", features = "nFeature_RNA", pt.size = pt_size, cols = c("grey90", "red")) + NoAxes()
p <- (p1-p2)/(p3-p4);p
fname <- paste0(ANALYSIS_ID, ".dimred_umap_qc_stats")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 10, height = 8.5);p;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 10*2.5, height = 8.5*2.5, res = 600); p; dev.off()## quartz_off_screen
## 2p <- DimPlot(object = se,
reduction = "umap", dims = c(1,2),
group.by = "subject_alias",
cols = colors_multi) +
NoAxes();
fname <- paste0(ANALYSIS_ID, ".dimred_umap_subject")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 6, height = 5);p;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 6*2.5, height = 5*2.5, res = 600); p; dev.off()## quartz_off_screen
## 2pt_size <- 0.25
plot_gene_umap <- function(se, gene, pt_size = 0.1){
p <- FeaturePlot(object = se, features = gene,
reduction = "umap", pt.size = pt_size,
slot = "scale.data", min.cutoff = 0,
cols = c("grey90", "red")) + NoAxes()
return(p)
}
plot_grid(plot_gene_umap(se, "SAA1"),
plot_gene_umap(se, "PLIN4"),
plot_gene_umap(se, "ADIPOQ"),
plot_gene_umap(se, "LEP"),
plot_gene_umap(se, "MRC1"),
plot_gene_umap(se, "CD68"),
plot_gene_umap(se, "IGKC"),
plot_gene_umap(se, "TPSB2"),
plot_gene_umap(se, "NKG7"),
ncol = 3)
pt_size <- 0.5
p1 <- DimPlot(object = se, dims = c(1,2), reduction = "umapICA", group.by = "subject_alias", pt.size = pt_size, cols = colors_multi) + NoAxes()
p2 <- DimPlot(object = se, dims = c(1,2), reduction = "umapICA", group.by = "condition", pt.size = pt_size, cols = colors_multi) + NoAxes()
p <- p1-p2;p
fname <- paste0(ANALYSIS_ID, ".dimred_umap_ica")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 10, height = 5);p;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 12*2.5, height = 5*2.5, res = 600); p; dev.off()## quartz_off_screen
## 2Clustering
Clustree
p <- clustree(se, prefix = "SCT_snn_res.", node_colour = "grey70");p
fname <- paste0(ANALYSIS_ID, ".clustering_clustree")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 12, height = 10);p;dev.off()## quartz_off_screen
## 2Annotations
p <- ggplot(c_anno, aes(seurat_clusters, 0, color = cluster_anno)) +
geom_point(size=3) +
scale_colour_manual(values = c_anno$cluster_color) +
scale_x_continuous(n.breaks = 20) +
scale_y_continuous(n.breaks = 2) +
theme_classic() +
theme(legend.position = 'top',
axis.title.y = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.line.y = element_blank());p
fname <- paste0(ANALYSIS_ID, ".clustering_annotationlegend")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 10, height = 2);p;dev.off()## quartz_off_screen
## 2cluster_prop <- merge(cluster_prop, c_anno, by="seurat_clusters")
se_markers <- merge(x = se_markers, y = c_anno, by.x="cluster", by.y="seurat_clusters")UMAP
dims_test <- paste0("SCT_snn_res.", c(0.8, 0.9, 1, 1.2, 1.6, 2))
DimPlot(object = se, dims = c(1,2), reduction = "umap", group.by = dims_test,
pt.size = 0.5,
label = T, label.size = 5, ncol = 3) & NoLegend()
p <- DimPlot(object = se,
reduction = "umap", dims = c(1,2),
group.by = "seurat_clusters",
cols = c_anno[order(as.numeric(c_anno$seurat_clusters)),]$cluster_color,
label = T,
label.size = 8,
repel = F) +
NoAxes(); p
p2 <- DimPlot(object = se,
reduction = "umap", dims = c(1,2),
group.by = "seurat_clusters",
cols = c_anno[order(as.numeric(c_anno$seurat_clusters)),]$cluster_color,
label = F) +
NoAxes() + NoLegend();
fname <- paste0(ANALYSIS_ID, ".clustering_umap")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 6, height = 5);p;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 6*2.5, height = 5*2.5, res = 600); p; dev.off()## quartz_off_screen
## 2# tiff(filename = file.path(DIR_FIG, paste0(fname, "2.tiff")), units = "pt", width = 190, height = 192, res = 600); p; dev.off() # TODO: !
fname <- paste0(ANALYSIS_ID, ".clustering_umap_b")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 4.8, height = 5);p2;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 4.8*2.5, height = 5*2.5, res = 600); p2; dev.off()## quartz_off_screen
## 2Spot proportions
d_plot <- cluster_prop
p1 <- ggplot(d_plot, aes(x = as.factor(subject), y = n, fill = as.factor(seurat_clusters))) +
geom_bar(stat = 'identity', colour=NA, position = "stack") +
scale_fill_manual(values = c_anno[order(c_anno$seurat_clusters), "cluster_color"]) +
labs(x="", y="# Spots", fill="") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
legend.position = "top", legend.text = element_text(size=6))
p2 <- ggplot(d_plot, aes(x = as.factor(seurat_clusters), y = n, fill = subject)) +
geom_bar(stat = 'identity', colour="white", position = "stack") +
scale_fill_manual(values = colors_multi) +
scale_y_log10() +
labs(x="", y="# Spots (log10)", fill="") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
legend.position = "top")
p3 <- ggplot(d_plot, aes(x = as.factor(seurat_clusters), y = n, fill = subject)) +
geom_bar(stat = 'identity', colour="white", position = "fill") +
scale_fill_manual(values = colors_multi) +
labs(x="", y="Spot proportions", fill="") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
legend.position = "top")
p <- plot_grid(p1, p2, p3, nrow = 1, rel_heights = c(0.55, 0.45));p
fname <- paste0(ANALYSIS_ID, ".clustering_countspots")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 10, height = 6);p;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 10*2.5, height = 6*2.5, res = 600); p; dev.off()## quartz_off_screen
## 2d_plot <- subset(cluster_prop, cluster_group %in% "Adipocyte")
p <- ggplot(d_plot, aes(x=as.numeric(bmi), y=cluster_pct_subject)) +
geom_line(color = "grey80") +
geom_point(size=2, aes(color=bmi)) +
scale_color_gradient(low = colors_main[1], high = colors_main[2]) +
scale_y_continuous(limits = c(0,15)) +
facet_grid(~cluster_anno) +
labs(y="Cluster proportion (%)", x="BMI") +
theme_classic() +
theme(legend.position = "none", strip.background = element_blank(), strip.text = element_text(face = "bold")); p
fname <- paste0(ANALYSIS_ID, ".clustering_countspots_corr-bmi")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 6, height = 2.5);p;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 6*2.5, height = 2.5*2.5, res = 600); p; dev.off()## quartz_off_screen
## 2Cluster marker genes
Cluster DEA
top_dotp <- se_markers %>% group_by(cluster) %>% dplyr::top_n(n = 2, wt = avg_logFC) %>% dplyr::arrange(as.numeric(cluster))
top_dotp_add <- se_markers %>% group_by(cluster) %>% dplyr::filter(gene %in% c("ADIPOQ", "SORBS1"))
top_dotp <- rbind(top_dotp, top_dotp_add) %>% dplyr::arrange((cluster_anno))
p1 <- DotPlot(se, features = rev(unique(top_dotp$gene)),
col.min = -2, col.max = 2,
group.by = "cluster_anno",
scale = T,
cols = c("grey90", "red")) + rotate() +
scale_colour_gradient2(low = "#441153", mid = "grey90", high = "#3CB67B") +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.title.y = element_blank()); p1
fname <- paste0(ANALYSIS_ID, ".markers_clusterdea_dotplot_a")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 7, height = 11); p1;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 7*2.5, height = 11*2.5, res = 600); p1; dev.off()## quartz_off_screen
## 2ggsave(filename = file.path(DIR_FIG, paste0(fname, ".eps")), plot = p1, width = 7, height = 11)p2 <- DotPlot(se, features = rev(unique(top_dotp$gene)),
col.min = -2, col.max = 2,
group.by = "cluster_anno",
scale = T,
cols = c("grey90", "red") ) +
scale_colour_gradient2(low = "#441153", mid = "grey90", high = "#3CB67B") +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.title.y = element_blank()); p2
fname <- paste0(ANALYSIS_ID, ".markers_clusterdea_dotplot_b")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 12, height = 5.4); p2;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 30, height = 8*1.8, res = 600); p2; dev.off()## quartz_off_screen
## 2ggsave(filename = file.path(DIR_FIG, paste0(fname, ".eps")), plot = p2, width = 12, height = 5.4)
# fname <- paste0(ANALYSIS_ID, ".markers_clusterdea_dotplot_test7")
# pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 12, height = 5.4); p2;dev.off()
# ggsave(filename = file.path(DIR_FIG, paste0(fname, ".eps")), plot = p2, width = 12, height = 5.4)top_hm <- se_markers %>% group_by(cluster) %>% dplyr::top_n(n = 5, wt = avg_logFC) %>% dplyr::arrange(as.numeric(cluster))
p <- DoHeatmap(se,
features = top_hm$gene,
group.colors = c_anno[order(c_anno$seurat_clusters), "cluster_color"],
angle = 0, hjust = 0, size = 5,
disp.min = -2.5, disp.max = 2.5) +
scale_fill_gradientn(colours = rev(RColorBrewer::brewer.pal(5, "RdBu")));
fname <- paste0(ANALYSIS_ID, ".markers_clusterdea_heatmap")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 22, height = 16);p;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 22*2.5, height = 16*2.5, res = 600); p; dev.off()## quartz_off_screen
## 2Pathway enrichment
top_pw_dot <- 3
top_pw_df_list <- list()
top_pw_list <- c()
for(c in 1:length(names(markers_seu_clusters))){
clus <- paste0("cluster_", (c+1))
if(!is.null(pw_results_go[[clus]])){
top_pws <- pw_results_go[[clus]][1:top_pw_dot,]$Description
top_pw_list <- c(top_pw_list, top_pws)
}
}
top_pw_list <- top_pw_list[!is.na(top_pw_list)]
top_pw_list <- unique(top_pw_list)
for(c in 1:length(names(markers_seu_clusters))){
clus <- paste0("cluster_", (c+1))
if(!is.null(pw_results_go[[clus]])){
pw_res <- pw_results_go[[clus]]@result
pw_res <- pw_res[pw_res$Description %in% top_pw_list, ]
df <- data.frame(Cluster = (c+1),
Pathway = pw_res$Description,
# Pathway_DB = "GO",
GeneRatio = pw_res$GeneRatio,
p.adjust = pw_res$p.adjust)
top_pw_df_list[[clus]] <- df
}
}
pw_df <- do.call("rbind", top_pw_df_list)
pw_df$cluster <- (as.numeric(pw_df$Cluster))
df_gr <- data.frame(do.call(rbind, strsplit(as.character(pw_df$GeneRatio), split = "/")), stringsAsFactors = F)
generatio <- round(as.numeric(df_gr$X1) / as.numeric(df_gr$X2), 2)
pw_df2 <- merge(pw_df, c_anno, by.x = "Cluster", by.y="seurat_clusters")
pw_df2$GeneRatio <- generatio
pw_df2 <- pw_df2[order(pw_df2$cluster_anno), ]
pw_df2 <- pw_df2 %>% group_by(cluster_anno) %>% dplyr::top_n(n = top_pw_dot, wt = GeneRatio) %>% dplyr::arrange(as.character(Pathway))p1 <- ggplot(pw_df2, aes(x=cluster_anno, y=Pathway, size=GeneRatio, color=-log10(p.adjust))) +
geom_point() +
scale_colour_gradient(low = "#c4e7d1", high = "#3CB67B", breaks=seq(0,20,4)) +
labs(x="", y="") +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.title.y = element_blank(),
axis.text.y = element_text(size=6),
panel.grid = element_line(),
panel.grid.major = element_line(colour = "grey95")); p1
fname <- paste0(ANALYSIS_ID, ".markers_pea_GO_a")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 8, height = 7);p1;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 8*2.5, height = 7*2.5, res = 600); p1; dev.off()## quartz_off_screen
## 2ggsave(filename = file.path(DIR_FIG, paste0(fname, ".eps")), plot = p1, width = 8, height = 7)p2 <- ggplot(pw_df2, aes(y=cluster_anno, x=Pathway, size=GeneRatio, color=-log10(p.adjust))) +
geom_point() +
scale_colour_gradient(low = "#c4e7d1", high = "#3CB67B", breaks=seq(0,20,4)) +
labs(x="", y="") +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.y = element_text(angle = 0, hjust = 1, vjust = 0.5),
axis.title.y = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size=6),
panel.grid = element_line(),
panel.grid.major = element_line(colour = "grey95")); p2
fname <- paste0(ANALYSIS_ID, ".markers_pea_GO_b")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 8, height = 6);p2;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 8*2.5, height = 6*2.5, res = 600); p2; dev.off()## quartz_off_screen
## 2ggsave(filename = file.path(DIR_FIG, paste0(fname, ".eps")), plot = p2, width = 8, height = 6)top_pw_dot <- 3
top_pw_df_list <- list()
top_pw_list <- c()
for(c in 1:length(names(markers_seu_clusters))){
clus <- paste0("cluster_", (c+1))
if(!is.null(pw_results_ra[[clus]])){
top_pws <- pw_results_ra[[clus]][1:top_pw_dot,]$Description
top_pw_list <- c(top_pw_list, top_pws)
}
}
top_pw_list <- top_pw_list[!is.na(top_pw_list)]
top_pw_list <- unique(top_pw_list)
for(c in 1:length(names(markers_seu_clusters))){
clus <- paste0("cluster_", (c+1))
if(!is.null(pw_results_ra[[clus]])){
pw_res <- pw_results_ra[[clus]]@result
pw_res <- pw_res[pw_res$Description %in% top_pw_list, ]
df <- data.frame(Cluster = (c+1),
Pathway = pw_res$Description,
GeneRatio = pw_res$GeneRatio,
p.adjust = pw_res$p.adjust)
top_pw_df_list[[clus]] <- df
}
}
pw_df_ra <- do.call("rbind", top_pw_df_list)
pw_df_ra$cluster <- (as.numeric(pw_df_ra$Cluster))
df_gr <- data.frame(do.call(rbind, strsplit(as.character(pw_df_ra$GeneRatio), split = "/")), stringsAsFactors = F)
generatio <- round(as.numeric(df_gr$X1) / as.numeric(df_gr$X2), 2)
pw_df_ra2 <- merge(pw_df_ra, c_anno, by.x = "Cluster", by.y="seurat_clusters")
pw_df_ra2$GeneRatio <- generatio
pw_df_ra2 <- pw_df_ra2[order(pw_df_ra2$cluster_anno), ]
pw_df_ra2 <- pw_df_ra2 %>% group_by(cluster_anno) %>% dplyr::top_n(n = top_pw_dot, wt = GeneRatio)p1 <- ggplot(pw_df_ra2, aes(x=cluster_anno, y=Pathway, size=GeneRatio, color=-log10(p.adjust))) +
geom_point() +
scale_colour_gradient(low = "#c4e7d1", high = "#3CB67B", breaks=seq(0,20,4)) +
labs(x="", y="") +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.title.y = element_blank(),
axis.text.y = element_text(size=6),
panel.grid = element_line(),
panel.grid.major = element_line(colour = "grey95")); p1
fname <- paste0(ANALYSIS_ID, ".markers_pea_RA_a")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 8, height = 7);p1;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 8*2.5, height = 7*2.5, res = 600); p1; dev.off()## quartz_off_screen
## 2ggsave(filename = file.path(DIR_FIG, paste0(fname, ".eps")), plot = p1, width = 8, height = 7)p2 <- ggplot(pw_df_ra2, aes(y=cluster_anno, x=Pathway, size=GeneRatio, color=-log10(p.adjust))) +
geom_point() +
scale_colour_gradient(low = "#c4e7d1", high = "#3CB67B", breaks=seq(0,20,4)) +
labs(x="", y="") +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.y = element_text(angle = 0, hjust = 1, vjust = 0.5),
axis.title.y = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size=6),
panel.grid = element_line(),
panel.grid.major = element_line(colour = "grey95")); p2
fname <- paste0(ANALYSIS_ID, ".markers_pea_RA_b")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 8, height = 6);p2;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 8*2.5, height = 6*2.5, res = 600); p2; dev.off()## quartz_off_screen
## 2ggsave(filename = file.path(DIR_FIG, paste0(fname, ".eps")), plot = p2, width = 8, height = 6)Other
LEP and ADIPOQ expression in subjects
All spots
d_plot <- se[[]]
d_plot$LEP <- GetAssayData(se, slot = "scale.data", assay = "SCT")["LEP", ]
d_plot$ADIPOQ <- GetAssayData(se, slot = "scale.data", assay = "SCT")["ADIPOQ", ]
p1 <- ggplot(d_plot, aes(x=factor(bmi), y=LEP, fill = gender)) +
geom_hline(yintercept = 0, color = "grey70") +
geom_violin(color=NA) +
geom_boxplot(width=0.1, outlier.size = 0.2) +
scale_fill_manual(values = colors_multi[c(2,6)]) +
labs(title="LEP", x="BMI", y="Norm. scaled expression") +
theme_classic() +
theme(plot.title = element_text(hjust=0.5, face = "bold"), legend.position = "bottom", legend.title = element_blank())
p2 <- ggplot(d_plot, aes(x=factor(bmi), y=ADIPOQ, fill = gender)) +
geom_hline(yintercept = 0, color = "grey70") +
geom_violin(color=NA) +
geom_boxplot(width=0.1, outlier.size = 0.2) +
scale_fill_manual(values = colors_multi[c(2,6)]) +
labs(title="ADIPOQ", x="BMI", y="Norm. scaled expression") +
theme_classic() +
theme(plot.title = element_text(hjust=0.5, face = "bold"), legend.position = "bottom", legend.title = element_blank())
p <- p1-p2;p
fname <- paste0(ANALYSIS_ID, ".other_geneexpr_LEP_ADIPOQ")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 8, height = 3.5);p;dev.off()## quartz_off_screen
## 2tiff(file = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 8*2.5, height = 3.5*2.5, res=600);p;dev.off()## quartz_off_screen
## 2Adipocyte spots
d_plot <- subset(se[[]], cluster_group == "Adipocyte")
d_plot$LEP <- GetAssayData(se[,rownames(d_plot)], slot = "scale.data", assay = "SCT")["LEP", ]
d_plot$ADIPOQ <- GetAssayData(se[,rownames(d_plot)], slot = "scale.data", assay = "SCT")["ADIPOQ", ]
p1 <- ggplot(d_plot, aes(x=factor(bmi), y=LEP, fill = gender)) +
geom_hline(yintercept = 0, color = "grey70") +
geom_violin(color=NA) +
geom_boxplot(width=0.2, outlier.size = 0.2) +
scale_fill_manual(values = colors_multi[c(2,6)]) +
facet_wrap(~cluster_anno) +
labs(title="LEP", x="BMI", y="Norm. scaled expression") +
theme_classic() +
theme(plot.title = element_text(hjust=0.5, face = "bold"), legend.position = "bottom", legend.title = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
strip.background = element_blank(), strip.text = element_text(face = "bold"))
p2 <- ggplot(d_plot, aes(x=factor(bmi), y=ADIPOQ, fill = gender)) +
geom_hline(yintercept = 0, color = "grey70") +
geom_violin(color=NA) +
geom_boxplot(width=0.2, outlier.size = 0.2) +
scale_fill_manual(values = colors_multi[c(2,6)]) +
facet_wrap(~cluster_anno) +
labs(title="ADIPOQ", x="BMI", y="Norm. scaled expression") +
theme_classic() +
theme(plot.title = element_text(hjust=0.5, face = "bold"), legend.position = "bottom", legend.title = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
strip.background = element_blank(), strip.text = element_text(face = "bold"))
p <- p1-p2;p
fname <- paste0(ANALYSIS_ID, ".other_geneexpr_LEP_ADIPOQ_adipocytes")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 12, height = 3.5);p;dev.off()## quartz_off_screen
## 2tiff(file = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 12*2.5, height = 3.5*2.5, res=600);p;dev.off()## quartz_off_screen
## 2M1 signauture
p <- ggplot(subset(df_M1_prop, ATM_M1_cat == "high"), aes(x = "", y = pct, fill = ATM_M1_cat)) +
geom_bar(stat = 'identity', position = "stack", width=0.7) +
scale_fill_manual(values = c("orangered", "grey70")) +
facet_wrap(~subject_alias, ncol = 5) +
labs(x="", fill="", y = "Proportion of total spots (%)", title="High M1 ATM") +
theme_minimal() +
geom_hline(yintercept = 0, color = "grey50") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5),
axis.text.y = element_text(size=16),
axis.title.y = element_text(size=18),
legend.position = "none",
panel.spacing = unit(2, "lines"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()); p
fname <- paste0(ANALYSIS_ID, ".other_M1sign_bar")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 8, height = 4);p;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 8*2.5, height = 4*2.5, res = 600); p; dev.off()## quartz_off_screen
## 2p <- ggplot(subset(df_M1_prop, ATM_M1_cat == "high"), aes(x=as.factor(condition), y=pct)) +
geom_boxplot(width=0.5) +
geom_point(size=2, aes(color=subject_alias)) +
# scale_color_gradient(low = colors_main[1], high = colors_main[2]) +
scale_color_manual(values = colors_multi) +
# ylim(min(d_plot$cluster_pct_subject), max(d_plot$cluster_pct_subject)) +
scale_y_continuous(limits = c(0,30)) +
# facet_grid(~subject_alias) +
labs(y="Proportion of total spots (%)", title="High M1 ATM", x="") +
theme_classic() +
theme(legend.position = "none", plot.title = element_text(hjust=0.5, face = "bold"),
axis.text.x = element_text(size=10)); p
fname <- paste0(ANALYSIS_ID, ".other_M1sign_box")
pdf(useDingbats = F, file = file.path(DIR_FIG, paste0(fname, ".pdf")), width = 3, height = 3);p;dev.off()## quartz_off_screen
## 2tiff(filename = file.path(DIR_FIG, paste0(fname, ".tiff")), units = "cm", width = 3*2.5, height = 3*2.5, res = 600); p; dev.off()## quartz_off_screen
## 2Wrap up
Save object
fname <- paste0("se-object.", ANALYSIS_ID, ".rds")
saveRDS(object = se, file = file.path(DIR_RES, fname))
# se <- readRDS(file = file.path(DIR_RES, fname))Session information
This analysis was last compiled on 2021-04-22.
sessionInfo()## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] org.Hs.eg.db_3.10.0 AnnotationDbi_1.48.0
## [3] rjson_0.2.20 rhdf5_2.30.1
## [5] knitr_1.30 DT_0.16
## [7] enrichplot_1.6.1 clusterProfiler_3.14.3
## [9] ReactomePA_1.30.0 clustree_0.4.3
## [11] ggraph_2.0.3 harmony_1.0
## [13] Rcpp_1.0.4.6 STutility_0.1.0
## [15] SingleCellExperiment_1.8.0 SummarizedExperiment_1.16.1
## [17] DelayedArray_0.12.3 BiocParallel_1.20.1
## [19] matrixStats_0.57.0 Biobase_2.46.0
## [21] GenomicRanges_1.38.0 GenomeInfoDb_1.22.1
## [23] IRanges_2.20.2 S4Vectors_0.24.4
## [25] BiocGenerics_0.32.0 Seurat_3.1.5
## [27] writexl_1.3.1 ggpubr_0.4.0
## [29] patchwork_1.1.0 cowplot_1.1.0
## [31] ggrepel_0.8.2 RColorBrewer_1.1-2
## [33] ggplot2_3.3.2 tidyr_1.1.2
## [35] Matrix_1.2-18
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.1 coda_0.19-4 bit64_4.0.5
## [4] irlba_2.3.3 data.table_1.13.2 rpart_4.1-15
## [7] RCurl_1.98-1.2 doParallel_1.0.16 generics_0.1.0
## [10] Rvcg_0.19.1 RSQLite_2.2.1 RANN_2.6.1
## [13] europepmc_0.4 imager_0.42.3 future_1.20.1
## [16] bit_4.0.4 xml2_1.3.2 spatstat.data_1.4-3
## [19] webshot_0.5.2 httpuv_1.5.4 viridis_0.5.1
## [22] Morpho_2.8 xfun_0.18 hms_0.5.3
## [25] evaluate_0.14 promises_1.1.1 progress_1.2.2
## [28] readxl_1.3.1 igraph_1.2.6 DBI_1.1.0
## [31] htmlwidgets_1.5.2 spdep_1.1-5 purrr_0.3.4
## [34] ellipsis_0.3.1 crosstalk_1.1.0.1 dplyr_1.0.2
## [37] backports_1.2.0 ggiraph_0.7.8 deldir_0.1-29
## [40] vctrs_0.3.5 ROCR_1.0-11 abind_1.4-5
## [43] withr_2.3.0 ggforce_0.3.2 triebeard_0.3.0
## [46] checkmate_2.0.0 sctransform_0.2.1 prettyunits_1.1.1
## [49] goftest_1.2-2 cluster_2.1.0 DOSE_3.12.0
## [52] ape_5.4-1 lazyeval_0.2.2 crayon_1.3.4
## [55] labeling_0.4.2 pkgconfig_2.0.3 units_0.6-7
## [58] tweenr_1.0.1 nlme_3.1-149 rlang_0.4.8
## [61] globals_0.13.1 lifecycle_0.2.0 miniUI_0.1.1.1
## [64] dbscan_1.1-5 akima_0.6-2.1 rsvd_1.0.3
## [67] cellranger_1.1.0 bmp_0.3 polyclip_1.10-0
## [70] lmtest_0.9-38 graph_1.64.0 tiff_0.1-5
## [73] urltools_1.7.3 raster_3.3-13 carData_3.0-4
## [76] Rhdf5lib_1.8.0 boot_1.3-25 zoo_1.8-8
## [79] ggridges_0.5.2 png_0.1-7 viridisLite_0.3.0
## [82] bitops_1.0-6 KernSmooth_2.23-17 blob_1.2.1
## [85] rgl_0.100.54 classInt_0.4-3 stringr_1.4.0
## [88] qvalue_2.18.0 manipulateWidget_0.10.1 parallelly_1.21.0
## [91] gridGraphics_0.5-0 jpeg_0.1-8.1 rstatix_0.6.0
## [94] ggsignif_0.6.0 reactome.db_1.70.0 scales_1.1.1
## [97] graphite_1.32.0 memoise_1.1.0 magrittr_2.0.1
## [100] plyr_1.8.6 ica_1.0-2 gdata_2.18.0
## [103] zlibbioc_1.32.0 compiler_3.6.3 fitdistrplus_1.1-1
## [106] XVector_0.26.0 LearnBayes_2.15.1 listenv_0.8.0
## [109] pbapply_1.4-3 MASS_7.3-53 mgcv_1.8-33
## [112] tidyselect_1.1.0 stringi_1.5.3 forcats_0.5.0
## [115] yaml_2.2.1 GOSemSim_2.12.1 grid_3.6.3
## [118] fastmatch_1.1-0 tools_3.6.3 future.apply_1.6.0
## [121] rio_0.5.16 uuid_0.1-4 foreach_1.5.1
## [124] foreign_0.8-75 gridExtra_2.3 farver_2.0.3
## [127] Rtsne_0.15 BiocManager_1.30.10 rvcheck_0.1.8
## [130] digest_0.6.27 shiny_1.5.0 car_3.0-10
## [133] broom_0.7.2 later_1.1.0.1 RcppAnnoy_0.0.17
## [136] httr_1.4.2 gdtools_0.2.2 readbitmap_0.1.5
## [139] sf_0.9-6 colorspace_2.0-0 tensor_1.5
## [142] reticulate_1.18 splines_3.6.3 uwot_0.1.9
## [145] expm_0.999-5 spatstat.utils_1.17-0 graphlayouts_0.7.0
## [148] sp_1.4-4 ggplotify_0.0.5 plotly_4.9.2.1
## [151] spData_0.3.8 systemfonts_0.3.2 xtable_1.8-4
## [154] jsonlite_1.7.1 spatstat_1.64-1 tidygraph_1.2.0
## [157] zeallot_0.1.0 R6_2.5.0 gmodels_2.18.1
## [160] pillar_1.4.7 htmltools_0.5.0 mime_0.9
## [163] glue_1.4.2 fastmap_1.0.1 class_7.3-17
## [166] codetools_0.2-16 fgsea_1.12.0 tsne_0.1-3
## [169] lattice_0.20-41 tibble_3.0.4 curl_4.3
## [172] leiden_0.3.5 colorRamps_2.3 gtools_3.8.2
## [175] magick_2.5.0 zip_2.1.1 GO.db_3.10.0
## [178] shinyjs_2.0.0 openxlsx_4.2.2 survival_3.2-7
## [181] rmarkdown_2.5 munsell_0.5.0 e1071_1.7-4
## [184] DO.db_2.9 GenomeInfoDbData_1.2.2 iterators_1.0.13
## [187] haven_2.3.1 reshape2_1.4.4 gtable_0.3.0se@commands## $SCTransform.RNA
## Command: SCTransform(se, variable.features.n = n_var_feat, verbose = T)
## Time: 2020-08-13 20:49:34
## assay : RNA
## new.assay.name : SCT
## do.correct.umi : TRUE
## variable.features.n : 7000
## variable.features.rv.th : 1.3
## do.scale : FALSE
## do.center : TRUE
## clip.range : -30.51612 30.51612
## conserve.memory : FALSE
## return.only.var.genes : TRUE
## seed.use : 1448145
## verbose : TRUE
##
## $RunPCA.SCT
## Command: RunPCA(object = se, verbose = T)
## Time: 2020-08-13 20:56:26
## assay : SCT
## npcs : 50
## rev.pca : FALSE
## weight.by.var : TRUE
## verbose : TRUE
## ndims.print : 1 2 3 4 5
## nfeatures.print : 30
## reduction.name : pca
## reduction.key : PC_
## seed.use : 42
##
## $RunICA.SCT
## Command: RunICA(object = se, verbose = T)
## Time: 2020-08-13 21:18:17
## assay : SCT
## nics : 50
## rev.ica : FALSE
## ica.function : icafast
## verbose : TRUE
## ndims.print : 1 2 3 4 5
## nfeatures.print : 30
## reduction.name : ica
## reduction.key : IC_
## seed.use : 42
##
## $Seurat..ProjectDim.SCT.harmony
## Command: Seurat::ProjectDim(object, reduction = reduction.save, overwrite = TRUE, verbose = FALSE)
## Time: 2020-08-13 21:54:11
## reduction : harmony
## assay : SCT
## dims.print : 1 2 3 4 5
## nfeatures.print : 20
## overwrite : TRUE
## do.center : FALSE
## verbose : FALSE
##
## $RunUMAP.SCT.harmony
## Command: RunUMAP(object = se, dims = dims_use, n.components = 3, reduction = red_use, n.neighbors = nneigh, reduction.name = "umap3d")
## Time: 2020-08-13 21:58:44
## dims : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
## reduction : harmony
## assay : SCT
## umap.method : uwot
## n.neighbors : 50
## n.components : 3
## metric : cosine
## learning.rate : 1
## min.dist : 0.3
## spread : 1
## set.op.mix.ratio : 1
## local.connectivity : 1
## repulsion.strength : 1
## negative.sample.rate : 5
## uwot.sgd : FALSE
## seed.use : 42
## angular.rp.forest : FALSE
## verbose : TRUE
## reduction.name : umap3d
## reduction.key : UMAP_
##
## $RunUMAP.SCT.ica
## Command: RunUMAP(object = se_ori_new, reduction = "ica", dims = dims_use, n.neighbors = nneigh, reduction.name = "umapICA", seed.use = 42)
## Time: 2020-11-23 11:05:29
## dims : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
## reduction : ica
## assay : SCT
## umap.method : uwot
## n.neighbors : 50
## n.components : 2
## metric : cosine
## learning.rate : 1
## min.dist : 0.3
## spread : 1
## set.op.mix.ratio : 1
## local.connectivity : 1
## repulsion.strength : 1
## negative.sample.rate : 5
## uwot.sgd : FALSE
## seed.use : 42
## angular.rp.forest : FALSE
## verbose : TRUE
## reduction.name : umapICA
## reduction.key : UMAP_
##
## $FindNeighbors.SCT.harmony
## Command: FindNeighbors(object = se, verbose = T, reduction = red_use, dims = dims_use)
## Time: 2020-12-02 15:02:54
## reduction : harmony
## dims : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
## assay : SCT
## k.param : 20
## compute.SNN : TRUE
## prune.SNN : 0.06666667
## nn.method : rann
## annoy.metric : euclidean
## nn.eps : 0
## verbose : TRUE
## force.recalc : FALSE
## do.plot : FALSE
## graph.name : SCT_nn SCT_snn
##
## $FindClusters
## Command: FindClusters(object = se, verbose = T, algorithm = 1, resolution = res)
## Time: 2020-12-02 15:03:49
## graph.name : SCT_snn
## modularity.fxn : 1
## resolution : 2
## method : matrix
## algorithm : 1
## n.start : 10
## n.iter : 10
## random.seed : 0
## group.singletons : TRUE
## verbose : TRUE